In [ ]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelEncoder
from scipy.stats import chi2_contingency,shapiro,spearmanr
from sklearn.preprocessing import StandardScaler,MinMaxScaler
#from sklearn.preprocessing import LabelEncode
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm  import  SVC
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score,confusion_matrix, roc_curve, auc
from imblearn.over_sampling import SMOTE

Charger le dataset¶

In [ ]:
data= pd.read_excel("/content/Base_Donnees_AVC 1.xltm")
data.head()
Out[ ]:
Genre Age Hypertension Maladie_Cardiaque Situation_Matrimoniale Type_travail Residence Taux_glucose_moyen IMC Statut_Fumer AVC
0 Male 67.0 0 1 Yes Private Urban 228.69 36.6 formerly smoked 1
1 Female 61.0 0 0 Yes Self-employed Rural 202.21 NaN never smoked 1
2 Male 80.0 0 1 Yes Private Rural 105.92 32.5 never smoked 1
3 Female 49.0 0 0 Yes Private Urban 171.23 34.4 smokes 1
4 Female 79.0 1 0 Yes Self-employed Rural 174.12 24.0 never smoked 1

Contexte¶

Selon l'Organisation mondiale de la santé (OMS), l'accident vasculaire cérébral (AVC) est la deuxième cause de décès dans le monde, responsable d'environ 11 % du total des décès.¶
Ce jeu de données est utilisé pour prédire si un patient est susceptible de subir un accident vasculaire cérébral (AVC) en fonction de paramètres d'entrée tels que le sexe, l'âge, diverses maladies et le tabagisme. Chaque ligne des données fournit des informations pertinentes sur le patient.¶

Description des variables¶

1) Genre : "Homme = Male", "Femme = Female" ou "Autre".¶
2) Age : âge du patient¶
3) Hypertension : 0 si le patient ne souffre pas d'hypertension, 1 si le patient souffre d'hypertension.¶
4) Maladie_cardiaque : 0 si le patient n'a pas de maladie cardiaque, 1 si le patient a une maladie cardiaque.¶
5) Situation_matrimoniale : "Non = No" ou "Oui = Yes".¶
6) Type_travail : "enfants = children", "Govt_jov", "Never_worked", "Private" ou "Self-employed".¶
7) Residence : "Rural" ou "Urbain".¶
8) Taux_glucose_moyen : taux moyen de glucose dans le sang¶
9) IMC : indice de masse corporelle¶
10) Statut_fumer : "anciennement fumeur = formely smokes", "jamais fumeur = never smoke", "fumeur = smokes" ou "inconnu = unknown ".¶
11) AVC : 1 si le patient a eu un AVC ou 0 sinon¶

Petite Inspection¶

In [ ]:
# Dimension de la base de donnée
data.shape
Out[ ]:
(5110, 11)
In [ ]:
# Information de la base de donnée
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5110 entries, 0 to 5109
Data columns (total 11 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   Genre                   5110 non-null   object 
 1   Age                     5110 non-null   float64
 2   Hypertension            5110 non-null   int64  
 3   Maladie_Cardiaque       5110 non-null   int64  
 4   Situation_Matrimoniale  5110 non-null   object 
 5   Type_travail            5110 non-null   object 
 6   Residence               5110 non-null   object 
 7   Taux_glucose_moyen      5110 non-null   float64
 8   IMC                     4909 non-null   float64
 9   Statut_Fumer            5110 non-null   object 
 10  AVC                     5110 non-null   int64  
dtypes: float64(3), int64(3), object(5)
memory usage: 439.3+ KB

Corriger quelques incoherences ou erreurs¶

In [ ]:
## Transformation de certains types de variables
data['Maladie_Cardiaque'] = data['Maladie_Cardiaque'].astype('category')

data['Hypertension'] = data['Hypertension'].astype('category')

data['AVC'] = data['AVC'].astype('category')
In [ ]:
data.duplicated().sum()
Out[ ]:
np.int64(0)
Il n'esxiste pas de duplication dans la base de donnée¶
In [ ]:
data.head()
Out[ ]:
Genre Age Hypertension Maladie_Cardiaque Situation_Matrimoniale Type_travail Residence Taux_glucose_moyen IMC Statut_Fumer AVC
0 Male 67.0 0 1 Yes Private Urban 228.69 36.6 formerly smoked 1
1 Female 61.0 0 0 Yes Self-employed Rural 202.21 NaN never smoked 1
2 Male 80.0 0 1 Yes Private Rural 105.92 32.5 never smoked 1
3 Female 49.0 0 0 Yes Private Urban 171.23 34.4 smokes 1
4 Female 79.0 1 0 Yes Self-employed Rural 174.12 24.0 never smoked 1
In [ ]:
data['AVC'].value_counts()
Out[ ]:
count
AVC
0 4861
1 249

In [ ]:
data['Statut_Fumer'].value_counts()
Out[ ]:
count
Statut_Fumer
never smoked 1892
Unknown 1544
formerly smoked 885
smokes 789

Pour la variable statut fumer ya des valeurs non renseigner nous allons les transformer en NAN ensuite les traiter¶
In [ ]:
## Valeur manquante
data.isnull().sum()
Out[ ]:
0
Genre 0
Age 0
Hypertension 0
Maladie_Cardiaque 0
Situation_Matrimoniale 0
Type_travail 0
Residence 0
Taux_glucose_moyen 0
IMC 201
Statut_Fumer 0
AVC 0

Il existe des valeurs manquantes sur les variables IMC et le statut des fumeur¶

Analyse exploratoire Avant prétraitement des données¶

In [ ]:
# Séparation des variables selon leur type
data_numeriques = data.select_dtypes(include=['int64', 'float64'])
data_categorielles = data.select_dtypes(include=['object', 'category'])

Pour les variables numériques¶

In [ ]:
## Résume descriptif
data.describe(include="number").T
Out[ ]:
count mean std min 25% 50% 75% max
Age 5110.0 43.226614 22.612647 0.08 25.000 45.000 61.00 82.00
Taux_glucose_moyen 5110.0 106.147677 45.283560 55.12 77.245 91.885 114.09 271.74
IMC 4909.0 28.893237 7.854067 10.30 23.500 28.100 33.10 97.60
In [ ]:
plt.figure(figsize=(14,4))
for col in data_numeriques.columns:
    plt.subplot(121)
    sns.set_theme(style="darkgrid")
    sns.histplot(data=data,x=col,kde=True)
    plt.title(f'Histogramme de la variable {col}')
    plt.subplot(122)
    sns.boxplot(data[col])
    plt.title(f'Boxplot de la variable {col}')
    plt.show()
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
In [ ]:
In [ ]:
for col in data_numeriques.columns:
    print(f"Skewness de la variable {col}: {data[col].skew():.2f}")
    print(f"Kurtosis de la variable {col}: {data[col].kurt():.2f}")

Skewness de la variable Age: -0.14
Kurtosis de la variable Age: -0.99
Skewness de la variable Taux_glucose_moyen: 1.57
Kurtosis de la variable Taux_glucose_moyen: 1.68
Skewness de la variable IMC: 1.06
Kurtosis de la variable IMC: 3.36

test de normalité (Shapiro-Wilk)¶

In [ ]:
for col in data_numeriques.columns:
    stat, p_value = shapiro(data[col].sample(500, random_state=42))

    if p_value < 0.05:
            print(f"La variable {col} ne suit pas la loi normale (p-value = {p_value})")
    else:
            print(f"La variable {col} suit la loi normale (p-value = {p_value})")

La variable Age ne suit pas la loi normale (p-value = 6.400304511076399e-09)
La variable Taux_glucose_moyen ne suit pas la loi normale (p-value = 2.3073166356644035e-25)
La variable IMC suit la loi normale (p-value = nan)
In [ ]:
for col in data_numeriques.columns:
    stat, p_value = shapiro(data[col])

    if p_value < 0.05:
            print(f"La variable {col} ne suit pas la loi normale (p-value = {p_value})")
    else:
            print(f"La variable {col} suit la loi normale (p-value = {p_value})")

La variable Age ne suit pas la loi normale (p-value = 1.3789355302240572e-32)
La variable Taux_glucose_moyen ne suit pas la loi normale (p-value = 1.795389063729762e-61)
La variable IMC suit la loi normale (p-value = nan)

/usr/local/lib/python3.11/dist-packages/scipy/stats/_axis_nan_policy.py:586: UserWarning: scipy.stats.shapiro: For N > 5000, computed p-value may not be accurate. Current N is 5110.
  res = hypotest_fun_out(*samples, **kwds)

Pour les variables catégorielles¶

In [ ]:
plt.figure(figsize=(5, 24))
sns.set_style("whitegrid", {'grid.linestyle': ':', 'axes.edgecolor': '0.4'})

for i, col in enumerate(data_categorielles.columns, 1):
    plt.subplot(len(data_categorielles.columns), 1, i)

    # Tri par fréquence
    order = data_categorielles[col].value_counts(dropna=False).index

    ax = sns.countplot(
        x=col,
        data=data_categorielles,
        hue = "AVC",
        order=order,
        palette='plasma_r',
        edgecolor='black',
        linewidth=0.5,
        saturation=0.9
    )

    # Annotations optimisées
    for p in ax.patches:
        height = p.get_height()
        ax.text(
            p.get_x() + p.get_width()/2,
            height + max(height*0.05, 0.5),  # Adaptation dynamique à la hauteur
            f'{int(height)}',
            ha='center',
            va='bottom',
            fontsize=9,
            color='black',
            bbox=dict(facecolor='white', alpha=0.8, edgecolor='none', boxstyle='round,pad=0.2')
        )

    # Paramètres esthétiques
    plt.title(f'Distribution de {col}', pad=12, fontsize=13, fontweight='semibold')
    plt.ylabel('Fréquence', fontsize=10)
    plt.xlabel('', fontsize=0)  # Suppression totale de l'axe X

    # Rotation et alignement des ticks
    plt.xticks(
        rotation=45,
        ha='right',
        fontsize=9,
        rotation_mode='anchor'  # Meilleur alignement
    )

    # Décoration des axes
    ax.spines[['top', 'right']].set_visible(False)
    ax.grid(axis='y', linestyle=':', alpha=0.6)

plt.tight_layout(pad=2.0)  # Réduction de l'espacement
plt.subplots_adjust(hspace=1)  # Espace vertical entre les subplots
plt.show()
No description has been provided for this image
In [ ]:
# prompt: met en commentaire la cellule precedente

#import matplotlib.pyplot as plt
#plt.figure(figsize=(20, 16))
#sns.set_style("whitegrid", {'grid.linestyle': ':', 'axes.edgecolor': '0.4'})

## Dictionnaire de renommage des variables
#noms_variables = {
#    'Hypertension': 'Hypertension', # Renommer Hypertension
#    'AVC': 'Accident Vasculaire Cérébral',
#    'Maladie_Cardiaque': 'Maladie Cardiaque', # Renommer Maladie_Cardiaque
#    'Situation_Matrimoniale': 'Situation Matrimoniale'
#}

## Dictionnaire de renommage des modalités
#noms_modalites = {
#    0: 'Non',
#    1: 'Oui',
#    '0': 'Non',
#    '1': 'Oui',
#
#}

## Variables à traiter spécifiquement (en utilisant les noms de colonnes originaux)
#variables_binaires = ['Hypertension', 'AVC', 'Maladie_Cardiaque']

## Disposition des graphiques
#n_cols = 4
#n_rows = (len(data_categorielles.columns) + n_cols - 1) // n_cols

#for i, col in enumerate(data_categorielles.columns, 1):
#    plt.subplot(n_rows, n_cols, i)
#
#    # Copie des données avec conversion des modalités
#    temp_data = data_categorielles.copy()
#
#    # Application du renommage pour les variables binaires
#    if col in variables_binaires:
#        # Assurez-vous que la colonne est du type str avant de remplacer les modalités
#        temp_data[col] = temp_data[col].astype(str).replace(noms_modalites)
#
#    # Tri par fréquence
#    order = temp_data[col].value_counts(dropna=False).index
#
#    # Gestion de la variable hue (AVC) - utilisez le nom de colonne original
#    hue_var = "AVC" if col != "AVC" else None
#    if hue_var:
#        # Appliquer le renommage des modalités au hue_var
#        temp_data[hue_var] = temp_data[hue_var].astype(str).replace(noms_modalites)
#
#    # Création du graphique
#    ax = sns.countplot(
#        x=col,
#        data=temp_data,
#        hue=hue_var, # Utiliser le nom de colonne original
#        order=order,
#        palette='plasma_r',
#        edgecolor='black',
#        linewidth=0.5,
#        saturation=0.9
#    )

#    # Annotations
#    for p in ax.patches:
#        height = p.get_height()
#        ax.text(
#            p.get_x() + p.get_width()/2,
#            height + max(height*0.05, 0.5),
#            f'{int(height)}',
#            ha='center',
#            va='bottom',
#            fontsize=9,
#            color='black',
#            bbox=dict(facecolor='white', alpha=0.8, edgecolor='none', boxstyle='round,pad=0.2')
#        )

#    # Titre avec nom de variable renommé
#    title = noms_variables.get(col, col) # Utiliser le nom de colonne original pour chercher dans le dict
#    plt.title(f'Distribution de {title}', pad=12, fontsize=13, fontweight='semibold')
#
#    # Axes
#    plt.ylabel('Fréquence', fontsize=10)
#    plt.xlabel('')
#    plt.xticks(rotation=45, ha='right', fontsize=9, rotation_mode='anchor')
#
#    # Légende
#    if ax.legend_:
#        handles, labels = ax.get_legend_handles_labels()
#        # Appliquer le renommage des modalités aux labels de la légende
#        new_labels = [noms_modalites.get(l, l) for l in labels]
#        ax.legend(handles, new_labels, title=noms_variables.get("AVC", "AVC")) # Utiliser le nom de colonne original pour le titre de la légende

#    ax.spines[['top', 'right']].set_visible(False)
#    ax.grid(axis='y', linestyle=':', alpha=0.6)

## Ajustement final
#plt.tight_layout(pad=3.0)
#plt.subplots_adjust(hspace=0.5, wspace=0.3)
#plt.show()
In [ ]:
In [ ]:
def analyse_variable_categorielle(col, df=data, rotate_xticks=True, max_categories=10):
    # Calcul des effectifs et pourcentages
    freq_table = df[col].value_counts()
    percent_table = round(freq_table / freq_table.sum() * 100, 2)
    stats_df = pd.DataFrame({'Effectifs': freq_table, 'Pourcentages (%)': percent_table})


    # Affichage du tableau
    print(f"\n--- {col.upper()} ---")
    display(stats_df)
    # Ajustement du nombre de catégories affichées
    top_categories = freq_table[:max_categories]






    # Affichage du tableau
    print(f"\n--- {col.upper()} ---")
    display(stats_df)
    # Ajustement du nombre de catégories affichées
    top_categories = freq_table[:max_categories]
In [ ]:
 for var in data_categorielles:
    analyse_variable_categorielle(var)

--- GENRE ---
Effectifs Pourcentages (%)
Genre
Female 2994 58.59
Male 2115 41.39
Other 1 0.02 
--- GENRE ---
Effectifs Pourcentages (%)
Genre
Female 2994 58.59
Male 2115 41.39
Other 1 0.02 
--- HYPERTENSION ---
Effectifs Pourcentages (%)
Hypertension
0 4612 90.25
1 498 9.75 
--- HYPERTENSION ---
Effectifs Pourcentages (%)
Hypertension
0 4612 90.25
1 498 9.75 
--- MALADIE_CARDIAQUE ---
Effectifs Pourcentages (%)
Maladie_Cardiaque
0 4834 94.6
1 276 5.4 
--- MALADIE_CARDIAQUE ---
Effectifs Pourcentages (%)
Maladie_Cardiaque
0 4834 94.6
1 276 5.4 
--- SITUATION_MATRIMONIALE ---
Effectifs Pourcentages (%)
Situation_Matrimoniale
Yes 3353 65.62
No 1757 34.38 
--- SITUATION_MATRIMONIALE ---
Effectifs Pourcentages (%)
Situation_Matrimoniale
Yes 3353 65.62
No 1757 34.38 
--- TYPE_TRAVAIL ---
Effectifs Pourcentages (%)
Type_travail
Private 2925 57.24
Self-employed 819 16.03
children 687 13.44
Govt_job 657 12.86
Never_worked 22 0.43 
--- TYPE_TRAVAIL ---
Effectifs Pourcentages (%)
Type_travail
Private 2925 57.24
Self-employed 819 16.03
children 687 13.44
Govt_job 657 12.86
Never_worked 22 0.43 
--- RESIDENCE ---
Effectifs Pourcentages (%)
Residence
Urban 2596 50.8
Rural 2514 49.2 
--- RESIDENCE ---
Effectifs Pourcentages (%)
Residence
Urban 2596 50.8
Rural 2514 49.2 
--- STATUT_FUMER ---
Effectifs Pourcentages (%)
Statut_Fumer
never smoked 1892 37.03
Unknown 1544 30.22
formerly smoked 885 17.32
smokes 789 15.44 
--- STATUT_FUMER ---
Effectifs Pourcentages (%)
Statut_Fumer
never smoked 1892 37.03
Unknown 1544 30.22
formerly smoked 885 17.32
smokes 789 15.44 
--- AVC ---
Effectifs Pourcentages (%)
AVC
0 4861 95.13
1 249 4.87 
--- AVC ---
Effectifs Pourcentages (%)
AVC
0 4861 95.13
1 249 4.87
In [ ]:
# Création des classes d'âge
#bins = [0, 30, 60, data['Age'].max()]
#labels = ['0-45', '46-60', f"61-{int(data['Age'].max())}"]
#data['Age_Classe'] = pd.cut(data['Age'], bins=bins, labels=labels, right=True, include_lowest=True)

# Calcul de la table de fréquence et de pourcentage pour l'âge classé
#age_freq = data['Age_Classe'].value_counts()
#age_percent = (age_freq / age_freq.sum() * 100).round(2)

# Création du DataFrame pour le tableau
#age_table = pd.DataFrame({'Effectifs': age_freq, 'Pourcentages (%)': age_percent})

# Affichage du tableau
#print("\n--- Distribution de l'Âge par Classe ---")
#display(age_table)

Préprocessing¶

Gestion des valeurs manquantes¶

Pour la variable IMC¶

In [ ]:
# Calcul de l'écart-type pour la colonne IMC
ecart_type = data['IMC'].std()
print(f"Écart-type de l'IMC : {ecart_type:.2f}")

Écart-type de l'IMC : 7.85
Nous constatons que la distribution de la variable IMC suit la loi normal et que l'ecart types n'est pas grand alors la série n'est pas trop dispersé autour de sa moyenne.De plus la moyenne est sensiblement égale à la médiane alors la moyenne peut être un bon indicateur pour l'imputation¶
In [ ]:
## Imputation par la moyenne
moy = data['IMC'].mean()
data['IMC'] = data['IMC'].fillna(moy)

Pour la variable statut fumeur¶

In [ ]:
data['Statut_Fumer'] =  data['Statut_Fumer'].replace('Unknown', np.nan)
data['Statut_Fumer'].value_counts()
Out[ ]:
count
Statut_Fumer
never smoked 1892
formerly smoked 885
smokes 789

In [ ]:
data.isnull().sum()
Out[ ]:
0
Genre 0
Age 0
Hypertension 0
Maladie_Cardiaque 0
Situation_Matrimoniale 0
Type_travail 0
Residence 0
Taux_glucose_moyen 0
IMC 0
Statut_Fumer 1544
AVC 0

In [ ]:
# Calculer le mode  pour cette variables
mode = data['Statut_Fumer'].mode()[0]

print(f"Mode de la variable statut fumeur : {mode}")

Mode de la variable statut fumeur : never smoked
In [ ]:
## Imputation par le mode
data['Statut_Fumer'] = data['Statut_Fumer'].fillna(mode)
In [ ]:
data.isnull().sum()
Out[ ]:
0
Genre 0
Age 0
Hypertension 0
Maladie_Cardiaque 0
Situation_Matrimoniale 0
Type_travail 0
Residence 0
Taux_glucose_moyen 0
IMC 0
Statut_Fumer 0
AVC 0

Gestion des valeurs abberantes¶

In [ ]:
def replace_outliers_in_columns(df, columns, method="median"):
    df_cleaned = df.copy()

    for col in columns:
        Q1 = df[col].quantile(0.25)
        Q3 = df[col].quantile(0.75)
        IQR = Q3 - Q1
        lower = Q1 - 1.5 * IQR
        upper = Q3 + 1.5 * IQR

        if method == "median":
            replacement_value = df[col].median()
        elif method == "mean":
            replacement_value = df[col].mean()

        if method in ["median", "mean"]:
            df_cleaned[col] = df[col].apply(
                lambda x: replacement_value if x < lower or x > upper else x
            )
        elif method == "bounds":
            df_cleaned[col] = df[col].apply(
                lambda x: lower if x < lower else upper if x > upper else x
            )
        else:
            raise ValueError("Méthode inconnue : utiliser 'mean', 'median' ou 'bounds'")

    return df_cleaned
In [ ]:
df= replace_outliers_in_columns(data, ["Age", "Taux_glucose_moyen", "IMC" ], method="bounds")

Analyse exploratoire aprés prétraitement des données¶

Analyse univarié¶

In [ ]:
## Résumé descriptif
df.describe().T
Out[ ]:
count mean std min 25% 50% 75% max
Age 5110.0 43.226614 22.612647 0.08 25.000 45.000 61.00 82.0000
Taux_glucose_moyen 5110.0 100.996204 33.214738 55.12 77.245 91.885 114.09 169.3575
IMC 5110.0 28.721613 7.119940 10.30 23.800 28.400 32.80 46.3000
In [ ]:
#Séparation des variables
df_numeriques = df.select_dtypes(include=['int64', 'float64'])
df_categorielles = df.select_dtypes(include=['object', 'category'])

Pour les variables numériques¶

In [ ]:
plt.figure(figsize=(14,4))
for col in df_numeriques.columns:
    plt.subplot(121)
    sns.histplot(data=df,x=col,kde=True)
    plt.title(f'Histogramme de la variable {col}')
    plt.subplot(122)
    sns.boxplot(df[col])
    plt.title(f'Boxplot de la variable {col}')
    plt.show()
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
In [ ]:
for col in df_numeriques.columns:
    print(f"Skewness de la variable {col}: {df[col].skew():.2f}")
    print(f"Kurtosis de la variable {col}: {df[col].kurt():.2f}")

Skewness de la variable Age: -0.14
Kurtosis de la variable Age: -0.99
Skewness de la variable Taux_glucose_moyen: 0.94
Kurtosis de la variable Taux_glucose_moyen: -0.17
Skewness de la variable IMC: 0.44
Kurtosis de la variable IMC: -0.08

Test de normalité¶

In [ ]:
for col in df_numeriques.columns:
    stat, p_value = shapiro(df[col].sample(500, random_state=42))

    if p_value < 0.05:
            print(f"La variable {col} ne suit pas la loi normale (p-value = {p_value})")
    else:
            print(f"La variable {col} suit la loi normale (p-value = {p_value})")

La variable Age ne suit pas la loi normale (p-value = 6.400304511076399e-09)
La variable Taux_glucose_moyen ne suit pas la loi normale (p-value = 3.115839080369874e-20)
La variable IMC ne suit pas la loi normale (p-value = 7.104313222108549e-07)

Pour les variables catégorielles¶

In [ ]:
# Tableau de résumé
df.describe(include=['category', 'object']).T
Out[ ]:
count unique top freq
Genre 5110 3 Female 2994
Hypertension 5110 2 0 4612
Maladie_Cardiaque 5110 2 0 4834
Situation_Matrimoniale 5110 2 Yes 3353
Type_travail 5110 5 Private 2925
Residence 5110 2 Urban 2596
Statut_Fumer 5110 3 never smoked 3436
AVC 5110 2 0 4861
In [ ]:
plt.figure(figsize=(5, 24))
sns.set_style("whitegrid", {'grid.linestyle': ':', 'axes.edgecolor': '0.4'})

for i, col in enumerate(df_categorielles.columns, 1):
    plt.subplot(len(df_categorielles.columns), 1, i)

    # Tri par fréquence
    order = df_categorielles[col].value_counts(dropna=False).index

    ax = sns.countplot(
        x=col,
        data=df_categorielles,
        order=order,
        palette='plasma_r',
        edgecolor='black',
        linewidth=0.5,
        saturation=0.9
    )

    # Annotations optimisées
    for p in ax.patches:
        height = p.get_height()
        ax.text(
            p.get_x() + p.get_width()/2,
            height + max(height*0.05, 0.5),  # Adaptation dynamique à la hauteur
            f'{int(height)}',
            ha='center',
            va='bottom',
            fontsize=9,
            color='black',
            bbox=dict(facecolor='white', alpha=0.8, edgecolor='none', boxstyle='round,pad=0.2')
        )

    # Paramètres esthétiques
    plt.title(f'Distribution de {col}', pad=12, fontsize=13, fontweight='semibold')
    plt.ylabel('Fréquence', fontsize=10)
    plt.xlabel('', fontsize=0)  # Suppression totale de l'axe X

    # Rotation et alignement des ticks
    plt.xticks(
        rotation=45,
        ha='right',
        fontsize=9,
        rotation_mode='anchor'  # Meilleur alignement
    )

    # Décoration des axes
    ax.spines[['top', 'right']].set_visible(False)
    ax.grid(axis='y', linestyle=':', alpha=0.6)

plt.tight_layout(pad=2.0)  # Réduction de l'espacement
plt.subplots_adjust(hspace=0.8)  # Espace vertical entre les subplots
plt.show()

/tmp/ipython-input-39-2017314915.py:10: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  ax = sns.countplot(
/tmp/ipython-input-39-2017314915.py:10: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  ax = sns.countplot(
/tmp/ipython-input-39-2017314915.py:10: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  ax = sns.countplot(
/tmp/ipython-input-39-2017314915.py:10: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  ax = sns.countplot(
/tmp/ipython-input-39-2017314915.py:10: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  ax = sns.countplot(
/tmp/ipython-input-39-2017314915.py:10: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  ax = sns.countplot(
/tmp/ipython-input-39-2017314915.py:10: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  ax = sns.countplot(
/tmp/ipython-input-39-2017314915.py:10: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  ax = sns.countplot(
No description has been provided for this image

Analyse descriptive bivariée¶

Tache à faire¶

1. Voir graphiquement la relation des variables par rapport à la variable target¶
2. Faire un test de d'indépendance de khi-deux entre chaque variable et la variable target¶
3. Faire un test d'independance de khi-deux¶
4. Matrice de corrélation¶
5. Nuage de point entre les variables¶

Entre Variable quantitative¶

Nuage de point¶
In [ ]:
# pair plot des variables continues
plt.figure(figsize = (10,10))
sns.pairplot(data = df,hue = "AVC",diag_kind = 'hist')
plt.suptitle('Pairplot des variables continues', y=1.02)
plt.show( )

<Figure size 1000x1000 with 0 Axes>
No description has been provided for this image

Corrélations entre variables numériques¶

In [ ]:
# Calcul de la matrice de corrélation
corr_matrix = df_numeriques.corr()

# Affichage du heatmap
plt.figure(figsize=(8, 6))
sns.heatmap(corr_matrix, annot=True, cmap="coolwarm", fmt=".2f", linewidths=0.5, linecolor='gray')
plt.title("📌 Corrélation entre variables numériques", fontsize=14)
plt.xticks(rotation=45)
plt.yticks(rotation=0)
plt.tight_layout()
plt.show()

/tmp/ipython-input-41-2124487701.py:10: UserWarning: Glyph 128204 (\N{PUSHPIN}) missing from font(s) DejaVu Sans.
  plt.tight_layout()
/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128204 (\N{PUSHPIN}) missing from font(s) DejaVu Sans.
  fig.canvas.print_figure(bytes_io, **kw)
No description has been provided for this image

Test de corrélation (spearman)¶

In [ ]:
def compute_significant_spearman_correlations(data, alpha=0.05):
    df_numeriquesss = data.select_dtypes(include=["int64", "float64"]).columns
    results = []

    for i, col1 in enumerate(df_numeriquesss):
        for col2 in df_numeriquesss[i+1:]:
            corr, p = spearmanr(data[col1], data[col2])
            if p < alpha:
                results.append({
                    "Variable 1": col1,
                    "Variable 2": col2,
                    "Corrélation": round(corr, 2),
                    "p-value": round(p, 4)
                })
            elif p >= alpha: # Ajout du elif pour inclure toutes les corrélations
                 results.append({
                    "Variable 1": col1,
                    "Variable 2": col2,
                    "Corrélation": round(corr, 2),
                    "p-value": round(p, 4)})

    return pd.DataFrame(results).sort_values(by="Corrélation", key=abs, ascending=False)
In [ ]:
# Appel à la fonction apllique à nos df
significant_spearman_corrs = compute_significant_spearman_correlations(df_numeriques)
display(significant_spearman_corrs)
Variable 1 Variable 2 Corrélation p-value
1 Age IMC 0.36 0.0
0 Age Taux_glucose_moyen 0.14 0.0
2 Taux_glucose_moyen IMC 0.11 0.0
Toutes les p-values sont < 0.05, ce qui indique que toutes les corrélations sont statistiquement significatives.¶
Age est modérément lié à l’IMC et faiblement au taux de glucose moyen.¶
IMC et taux de glucose moyen sont faiblement associés.¶

Entre variable catégorielle¶

In [ ]:
cat_vars = ['Genre', 'Hypertension', 'Maladie_Cardiaque', 'Situation_Matrimoniale',
            'Type_travail', 'Residence', 'Statut_Fumer']

plt.figure(figsize=(16, 10))  # Ajustez la hauteur selon vos besoins

# Première ligne avec 4 graphiques
for i, var in enumerate(cat_vars[:4], 1):
    plt.subplot(2, 4, i)  # 2 lignes, 4 colonnes, position i
    sns.countplot(data=df, x='AVC', hue=var, palette='plasma_r',
                  edgecolor='black',
                  linewidth=0.5,
                  saturation=0.9)
    plt.title(f"Répartition de AVC selon {var}")
    plt.xlabel("AVC")
    plt.ylabel("Nombre d'observations")
    plt.legend(title=var, loc='upper right')

# Deuxième ligne avec 3 graphiques (centrés)
for i, var in enumerate(cat_vars[4:], 5):  # Commence à la position 5
    plt.subplot(2, 4, i)  # 2 lignes, 4 colonnes, positions 5,6,7
    sns.countplot(data=df, x='AVC', hue=var, palette='plasma_r',
                  edgecolor='black',
                  linewidth=0.5,
                  saturation=0.9)

    plt.title(f"Répartition de AVC selon {var}")
    plt.xlabel("AVC")
    plt.ylabel("Nombre d'observations")
    plt.legend(title=var, loc='upper right')

# Masquer le dernier subplot (8ème position) s'il n'est pas utilisé
if len(cat_vars) == 7:
    plt.subplot(2, 4, 8)
    plt.axis('off')

plt.tight_layout()
plt.show()
No description has been provided for this image
In [ ]:
cat_vars = ['Genre', 'Hypertension', 'Maladie_Cardiaque', 'Situation_Matrimoniale',
            'Type_travail', 'Residence', 'Statut_Fumer']

# Création de la figure avec une grille 2x4
plt.figure(figsize=(18, 12))

# Première ligne: 4 graphiques
for i, var in enumerate(cat_vars[:4], 1):
    plt.subplot(2, 4, i)
    ax = sns.countplot(data=df, x='AVC', hue=var, palette='plasma_r',
                      edgecolor='black', linewidth=0.5, saturation=0.9)
    plt.title(f"AVC par {var}", fontsize=12, pad=10)
    plt.xlabel("AVC", fontsize=10)
    plt.ylabel("Count", fontsize=10)
    plt.legend(title=var, bbox_to_anchor=(1.05, 1), title_fontsize=9)

    # Annotations optimisées
    for p in ax.patches:
        height = p.get_height()
        ax.text(
            p.get_x() + p.get_width()/2,
            height + max(height*0.05, 0.5),  # Adaptation dynamique à la hauteur
            f'{int(height)}',
            ha='center',
            va='bottom',
            fontsize=9,
            color='black',
            bbox=dict(facecolor='white', alpha=0.8, edgecolor='none', boxstyle='round,pad=0.2')
        )

# Deuxième ligne: 3 graphiques centrés
for i, var in enumerate(cat_vars[4:], 5):
    plt.subplot(2, 4, i)
    ax = sns.countplot(data=df, x='AVC', hue=var, palette='plasma_r',
                      edgecolor='black', linewidth=0.5, saturation=0.9)
    plt.title(f"AVC par {var}", fontsize=12, pad=10)
    plt.xlabel("AVC", fontsize=10)
    plt.ylabel("Count", fontsize=10)
    plt.legend(title=var, bbox_to_anchor=(1.05, 1), title_fontsize=9)

    # Annotations optimisées
    for p in ax.patches:
        height = p.get_height()
        ax.text(
            p.get_x() + p.get_width()/2,
            height + max(height*0.05, 0.5),  # Adaptation dynamique à la hauteur
            f'{int(height)}',
            ha='center',
            va='bottom',
            fontsize=9,
            color='black',
            bbox=dict(facecolor='white', alpha=0.8, edgecolor='none', boxstyle='round,pad=0.2')
        )

# Désactiver le 8ème subplot
plt.subplot(2, 4, 8)
plt.axis('off')

# Ajustement de l'espacement
plt.tight_layout(pad=3.0)
plt.suptitle("Répartition des cas d'AVC selon différentes caractéristiques", y=1.03, fontsize=14, weight='bold')
plt.show()
No description has been provided for this image

Test d'independance de khi-deux¶

In [ ]:
for var in df_categorielles.columns[:-1]:
    table_contegence = pd.crosstab(df[var], df['AVC'])
    print(f"Tableau croisé pour {var}:\n", table_contegence)

    # Test du Chi-carré
    chi2, p, dof, expected = chi2_contingency(table_contegence)
    print(f"Test du Chi-carré pour {var}:")
    print(f"  Chi2 statistic: {chi2}")
    print(f"  p-value: {p}")
    if p < 0.05:
         print(f"il existe une relation significative entre {var} et le type AVC.\n\n\n")
    else:

        print(f"Aucune relation significative détecté entre {var} et le type AVC.\n\n")

Tableau croisé pour Genre:
 AVC        0    1
Genre            
Female  2853  141
Male    2007  108
Other      1    0
Test du Chi-carré pour Genre:
  Chi2 statistic: 0.47258662884530234
  p-value: 0.7895490538408245
Aucune relation significative détecté entre Genre et le type AVC.


Tableau croisé pour Hypertension:
 AVC              0    1
Hypertension           
0             4429  183
1              432   66
Test du Chi-carré pour Hypertension:
  Chi2 statistic: 81.6053682482931
  p-value: 1.661621901511823e-19
il existe une relation significative entre Hypertension et le type AVC.



Tableau croisé pour Maladie_Cardiaque:
 AVC                   0    1
Maladie_Cardiaque           
0                  4632  202
1                   229   47
Test du Chi-carré pour Maladie_Cardiaque:
  Chi2 statistic: 90.25956125843324
  p-value: 2.0887845685229236e-21
il existe une relation significative entre Maladie_Cardiaque et le type AVC.



Tableau croisé pour Situation_Matrimoniale:
 AVC                        0    1
Situation_Matrimoniale           
No                      1728   29
Yes                     3133  220
Test du Chi-carré pour Situation_Matrimoniale:
  Chi2 statistic: 58.923890259034195
  p-value: 1.6389021142314745e-14
il existe une relation significative entre Situation_Matrimoniale et le type AVC.



Tableau croisé pour Type_travail:
 AVC               0    1
Type_travail            
Govt_job        624   33
Never_worked     22    0
Private        2776  149
Self-employed   754   65
children        685    2
Test du Chi-carré pour Type_travail:
  Chi2 statistic: 49.163511976675295
  p-value: 5.397707801896119e-10
il existe une relation significative entre Type_travail et le type AVC.



Tableau croisé pour Residence:
 AVC           0    1
Residence           
Rural      2400  114
Urban      2461  135
Test du Chi-carré pour Residence:
  Chi2 statistic: 1.0816367471627524
  p-value: 0.29833169286876987
Aucune relation significative détecté entre Residence et le type AVC.


Tableau croisé pour Statut_Fumer:
 AVC                 0    1
Statut_Fumer              
formerly smoked   815   70
never smoked     3299  137
smokes            747   42
Test du Chi-carré pour Statut_Fumer:
  Chi2 statistic: 23.766301101998046
  p-value: 6.905788734861923e-06
il existe une relation significative entre Statut_Fumer et le type AVC.

Entre variable quantitative et qualitative¶

In [ ]:
print(df.groupby('Genre')['Age'].describe())
# Boxplot
sns.boxplot(x='Genre', y='Age', data=df, palette="Set2")
plt.title("🛑 L'age selon le genre")
plt.show()

         count       mean        std    min   25%   50%   75%   max
Genre                                                              
Female  2994.0  43.757395  21.966561   0.08  27.0  44.0  61.0  82.0
Male    2115.0  42.483385  23.484066   0.08  22.0  46.0  61.0  82.0
Other      1.0  26.000000        NaN  26.00  26.0  26.0  26.0  26.0

/tmp/ipython-input-47-1761252738.py:3: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='Genre', y='Age', data=df, palette="Set2")
/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128721 (\N{OCTAGONAL SIGN}) missing from font(s) DejaVu Sans.
  fig.canvas.print_figure(bytes_io, **kw)
No description has been provided for this image

Test t de Student¶

In [ ]:
import scipy.stats as stats
from scipy.stats import mannwhitneyu
# Séparation des groupes
hommes = df[df['Genre'] == 'Homme']['Age']
femmes = df[df['Genre'] == 'Femme']['Age']
t_stat, p_value = stats.ttest_ind(hommes, femmes, equal_var=False)
print(f"T-statistique = {t_stat:.3f}, p-value = {p_value:.4f}")

if p_value < 0.05:
    print("L'age diffère significativement selon le genre.")
else:
    print("Aucune différence significative de l'age selon le genre.")

T-statistique = nan, p-value = nan
Aucune différence significative de l'age selon le genre.

/usr/local/lib/python3.11/dist-packages/scipy/_lib/deprecation.py:234: SmallSampleWarning: One or more sample arguments is too small; all returned values will be NaN. See documentation for sample size requirements.
  return f(*args, **kwargs)
AVC et Taux_glucose_moyen¶
In [ ]:
Les distribution des groupes ne suivent pas la loi normale utilison les test non paramétrique¶

AVC & Age¶

In [ ]:
sns.histplot(df[df['AVC'] == 0]['Age'], kde=True, color='blue', label='Sans AVC')
sns.histplot(df[df['AVC'] == 1]['Age'], kde=True, color='red', label='Avec AVC')
plt.legend()
plt.title("Histogramme de l'Age par groupe AVC")
plt.show()

# Groupe sans AVC
stat1, p1 = shapiro(df[df['AVC'] == 0]['Age'])
print(f"Sans AVC - Stat={stat1:.3f}, p={p1:.3f}")

# Groupe avec AVC
stat2, p2 = shapiro(df[df['AVC'] == 1]['Age'])
print(f"Avec AVC - Stat={stat2:.3f}, p={p2:.3f}")
No description has been provided for this image 
Sans AVC - Stat=0.970, p=0.000
Avec AVC - Stat=0.878, p=0.000
In [ ]:
print(df.groupby('AVC')['Age'].describe())
# Boxplot
sns.boxplot(x='AVC', y='Age', data=df, palette="Set2")
plt.title("🛑 Distribution de l'âge selon l'AVC")
plt.xlabel("AVC (0=Non, 1=Oui)")
plt.show()

      count       mean        std   min   25%   50%   75%   max
AVC                                                            
0    4861.0  41.971545  22.291940  0.08  24.0  43.0  59.0  82.0
1     249.0  67.728193  12.727419  1.32  59.0  71.0  78.0  82.0

/tmp/ipython-input-50-145994358.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  print(df.groupby('AVC')['Age'].describe())
/tmp/ipython-input-50-145994358.py:3: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='AVC', y='Age', data=df, palette="Set2")
/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128721 (\N{OCTAGONAL SIGN}) missing from font(s) DejaVu Sans.
  fig.canvas.print_figure(bytes_io, **kw)
No description has been provided for this image

Test de Man whitney¶

In [ ]:
# Séparation des groupes
age_sans_avc = df[df['AVC'] == 0]['Age']
age_avec_avc = df[df['AVC'] == 1]['Age']

t_stat, p_value = stats.mannwhitneyu(age_sans_avc, age_avec_avc)
print(f"U-statistique = {t_stat:.3f}, p-value = {p_value:.4f}")

if p_value < 0.05:
    print("Différence significative de l'âge moyen entre les groupes AVC.")
else:
    print("Pas de différence significative de l'âge moyen entre les groupes AVC.")

U-statistique = 200263.500, p-value = 0.0000
Différence significative de l'âge moyen entre les groupes AVC.

AVC & Taux de glycemie¶

In [ ]:
sns.histplot(df[df['AVC'] == 0]['Taux_glucose_moyen'], kde=True, color='blue', label='Sans AVC')
sns.histplot(df[df['AVC'] == 1]['Taux_glucose_moyen'], kde=True, color='red', label='Avec AVC')
plt.legend()
plt.title("Histogramme de la glycémie moyenne par groupe AVC")
plt.show()

# Groupe sans AVC
stat1, p1 = shapiro(df[df['AVC'] == 0]['Taux_glucose_moyen'])
print(f"Sans AVC - Stat={stat1:.3f}, p={p1:.3f}")

# Groupe avec AVC
stat2, p2 = shapiro(df[df['AVC'] == 1]['Taux_glucose_moyen'])
print(f"Avec AVC - Stat={stat2:.3f}, p={p2:.3f}")
No description has been provided for this image 
Sans AVC - Stat=0.878, p=0.000
Avec AVC - Stat=0.832, p=0.000
In [ ]:
# Moyenne et écart-type de la glycémie selon AVC
print(df.groupby('AVC')['Taux_glucose_moyen'].describe())
sns.boxplot(x='AVC', y='Taux_glucose_moyen', data=df, palette="Set2")
plt.title("🛑 Distribution du taux de glycémie selon l'AVC")
plt.xlabel("AVC (0=Non, 1=Oui)")
plt.show()

      count        mean        std    min    25%     50%       75%       max
AVC                                                                         
0    4861.0  100.126890  32.484047  55.12  77.12   91.47  112.8300  169.3575
1     249.0  117.967028  41.766465  56.11  79.79  105.22  169.3575  169.3575

/tmp/ipython-input-53-2483663623.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  print(df.groupby('AVC')['Taux_glucose_moyen'].describe())
/tmp/ipython-input-53-2483663623.py:3: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='AVC', y='Taux_glucose_moyen', data=df, palette="Set2")
/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128721 (\N{OCTAGONAL SIGN}) missing from font(s) DejaVu Sans.
  fig.canvas.print_figure(bytes_io, **kw)
No description has been provided for this image

Test de Man Whitney¶

In [ ]:
## séparation des groupes
glicemie_sans_avc = df[df['AVC'] == 0]['Taux_glucose_moyen']
glicemie_avec_avc = df[df['AVC'] == 1]['Taux_glucose_moyen']

t_stat, p_value = stats.mannwhitneyu(glicemie_sans_avc, glicemie_avec_avc)
print(f"Statistique t : {t_stat:.2f}")
print(f"p-value : {p_value:.4f}")

if p_value < 0.05:
    print("La différence de glycémie moyenne entre les groupes est significative.")
else:
    print("Aucune différence significative de glycémie moyenne entre les groupes.")

Statistique t : 471779.50
p-value : 0.0000
La différence de glycémie moyenne entre les groupes est significative.

AVC & IMC¶

In [ ]:
sns.histplot(df[df['AVC'] == 0]['IMC'], kde=True, color='blue', label='Sans AVC')
sns.histplot(df[df['AVC'] == 1]['IMC'], kde=True, color='red', label='Avec AVC')
plt.legend()
plt.title("Histogramme de l'IMC par groupe AVC")
plt.show()

# Groupe sans AVC
stat1, p1 = shapiro(df[df['AVC'] == 0]['IMC'])
print(f"Sans AVC - Stat={stat1:.3f}, p={p1:.3f}")

# Groupe avec AVC
stat2, p2 = shapiro(df[df['AVC'] == 1]['IMC'])
print(f"Avec AVC - Stat={stat2:.3f}, p={p2:.3f}")
No description has been provided for this image 
Sans AVC - Stat=0.980, p=0.000
Avec AVC - Stat=0.941, p=0.000
In [ ]:
print(df.groupby('AVC')['IMC'].describe())
sns.boxplot(x='AVC', y='IMC', data=df, palette="Set2")
plt.title("🛑 Distribution de l'IMC selon l'AVC")
plt.xlabel("AVC (0=Non, 1=Oui)")
plt.show()

      count       mean       std   min   25%        50%   75%   max
AVC                                                                
0    4861.0  28.647873  7.180836  10.3  23.6  28.300000  32.8  46.3
1     249.0  30.161163  5.625364  16.9  27.0  28.893237  32.5  46.3

/tmp/ipython-input-56-3758547483.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  print(df.groupby('AVC')['IMC'].describe())
/tmp/ipython-input-56-3758547483.py:2: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='AVC', y='IMC', data=df, palette="Set2")
/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128721 (\N{OCTAGONAL SIGN}) missing from font(s) DejaVu Sans.
  fig.canvas.print_figure(bytes_io, **kw)
No description has been provided for this image

Test de Man Whitney¶

In [ ]:
## séparation des groupes
imc_sans_avc = df[df['AVC'] == 0]['IMC']
imc_avec_avc = df[df['AVC'] == 1]['IMC']

t_stat, p_value = stats.mannwhitneyu(imc_sans_avc, imc_avec_avc)
print(f"Statistique t : {t_stat:.2f}")
print(f"p-value : {p_value:.4f}")

if p_value < 0.05:
    print("La différence de l'IMC entre les groupes est significative.")
else:
    print("Aucune différence significative de l'IMC entre les groupes.")

Statistique t : 515877.00
p-value : 0.0001
La différence de l'IMC entre les groupes est significative.
In [ ]:
In [ ]:
In [ ]:
from scipy.stats import shapiro, levene, ttest_ind, mannwhitneyu

def test_numeriques_vs_avc(df, var_cible='AVC'):
    # Sélection des variables numériques sauf la variable cible
    numeriques = df.select_dtypes(include=['float64', 'int64']).columns.tolist()
    if var_cible in numeriques:
        numeriques.remove(var_cible)

    print(f"Variables numériques analysées vs {var_cible} : {numeriques}\n")

    for var in numeriques:
        print(f"--- Analyse de la variable : {var} ---")

        # 1. Visualisation boxplot en premier
        plt.figure(figsize=(6,4))
        sns.boxplot(x=var_cible, y=var, data=df, palette="Set2")
        plt.title(f"Distribution de {var} selon {var_cible}")
        plt.xlabel(f"{var_cible} (0 = Non, 1 = Oui)")
        plt.ylabel(var)
        plt.show()
        groupe0 = df[df[var_cible] == 0][var].dropna()
        groupe1 = df[df[var_cible] == 1][var].dropna()

        # Test de normalité
        p_norm_0 = shapiro(groupe0).pvalue
        p_norm_1 = shapiro(groupe1).pvalue

        # Test d'homogénéité des variances
        p_levene = levene(groupe0, groupe1).pvalue

        print(f"Normalité p-values : groupe 0 = {p_norm_0:.4f}, groupe 1 = {p_norm_1:.4f}")
        print(f"Homogénéité des variances (Levene) p-value : {p_levene:.4f}")

        # Choix du test
        if p_norm_0 > 0.05 and p_norm_1 > 0.05 and p_levene > 0.05:
            # Test t de Student
            stat, p_val = ttest_ind(groupe0, groupe1, equal_var=True)
            test_name = "Test t de Student"
        else:
            # Test de Mann-Whitney
            stat, p_val = mannwhitneyu(groupe0, groupe1)
            test_name = "Test de Mann-Whitney"

        print(f"{test_name} : stat = {stat:.4f}, p-value = {p_val:.4f}")
        if p_val < 0.05:
            print(f"--> Différence significative détectée pour la variable {var}\n")
        else:
            print(f"--> Pas de différence significative pour la variable {var}\n")

        #
In [ ]:
test_numeriques_vs_avc(df)

Variables numériques analysées vs AVC : ['Age', 'Taux_glucose_moyen', 'IMC']

--- Analyse de la variable : Age ---

/tmp/ipython-input-58-1001718392.py:16: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x=var_cible, y=var, data=df, palette="Set2")
No description has been provided for this image 
Normalité p-values : groupe 0 = 0.0000, groupe 1 = 0.0000
Homogénéité des variances (Levene) p-value : 0.0000
Test de Mann-Whitney : stat = 200263.5000, p-value = 0.0000
--> Différence significative détectée pour la variable Age

--- Analyse de la variable : Taux_glucose_moyen ---

/tmp/ipython-input-58-1001718392.py:16: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x=var_cible, y=var, data=df, palette="Set2")
No description has been provided for this image 
Normalité p-values : groupe 0 = 0.0000, groupe 1 = 0.0000
Homogénéité des variances (Levene) p-value : 0.0000
Test de Mann-Whitney : stat = 471779.5000, p-value = 0.0000
--> Différence significative détectée pour la variable Taux_glucose_moyen

--- Analyse de la variable : IMC ---

/tmp/ipython-input-58-1001718392.py:16: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x=var_cible, y=var, data=df, palette="Set2")
No description has been provided for this image 
Normalité p-values : groupe 0 = 0.0000, groupe 1 = 0.0000
Homogénéité des variances (Levene) p-value : 0.0000
Test de Mann-Whitney : stat = 515877.0000, p-value = 0.0001
--> Différence significative détectée pour la variable IMC

Encodage¶

In [ ]:
df.head()
Out[ ]:
Genre Age Hypertension Maladie_Cardiaque Situation_Matrimoniale Type_travail Residence Taux_glucose_moyen IMC Statut_Fumer AVC
0 Male 67.0 0 1 Yes Private Urban 169.3575 36.600000 formerly smoked 1
1 Female 61.0 0 0 Yes Self-employed Rural 169.3575 28.893237 never smoked 1
2 Male 80.0 0 1 Yes Private Rural 105.9200 32.500000 never smoked 1
3 Female 49.0 0 0 Yes Private Urban 169.3575 34.400000 smokes 1
4 Female 79.0 1 0 Yes Self-employed Rural 169.3575 24.000000 never smoked 1
In [ ]:
label_encoder = LabelEncoder()
df['Genre'] = label_encoder.fit_transform(df['Genre'])
df['Genre'] = df['Genre'].astype('category')
df['Type_travail'] = label_encoder.fit_transform(df['Type_travail'])
df['Type_travail'] = df['Type_travail'].astype('category')
df['Situation_Matrimoniale'] = label_encoder.fit_transform(df['Situation_Matrimoniale'])
df['Situation_Matrimoniale'] = df['Situation_Matrimoniale'].astype('category')
df['Residence'] = label_encoder.fit_transform(df['Residence'])
df['Residence'] = df['Residence'].astype('category')
df['Statut_Fumer'] = label_encoder.fit_transform(df['Statut_Fumer'])
df['Statut_Fumer'] = df['Statut_Fumer'].astype('category')
df.head()
Out[ ]:
Genre Age Hypertension Maladie_Cardiaque Situation_Matrimoniale Type_travail Residence Taux_glucose_moyen IMC Statut_Fumer AVC
0 1 67.0 0 1 1 2 1 169.3575 36.600000 0 1
1 0 61.0 0 0 1 3 0 169.3575 28.893237 1 1
2 1 80.0 0 1 1 2 0 105.9200 32.500000 1 1
3 0 49.0 0 0 1 2 1 169.3575 34.400000 2 1
4 0 79.0 1 0 1 3 0 169.3575 24.000000 1 1
In [ ]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5110 entries, 0 to 5109
Data columns (total 11 columns):
 #   Column                  Non-Null Count  Dtype   
---  ------                  --------------  -----   
 0   Genre                   5110 non-null   category
 1   Age                     5110 non-null   float64 
 2   Hypertension            5110 non-null   category
 3   Maladie_Cardiaque       5110 non-null   category
 4   Situation_Matrimoniale  5110 non-null   category
 5   Type_travail            5110 non-null   category
 6   Residence               5110 non-null   category
 7   Taux_glucose_moyen      5110 non-null   float64 
 8   IMC                     5110 non-null   float64 
 9   Statut_Fumer            5110 non-null   category
 10  AVC                     5110 non-null   category
dtypes: category(8), float64(3)
memory usage: 160.9 KB
In [ ]:
df_categorielles = df.select_dtypes(include=['category'])
df_categorielles
Out[ ]:
Genre Hypertension Maladie_Cardiaque Situation_Matrimoniale Type_travail Residence Statut_Fumer AVC
0 1 0 1 1 2 1 0 1
1 0 0 0 1 3 0 1 1
2 1 0 1 1 2 0 1 1
3 0 0 0 1 2 1 2 1
4 0 1 0 1 3 0 1 1
... ... ... ... ... ... ... ... ...
5105 0 1 0 1 2 1 1 0
5106 0 0 0 1 3 1 1 0
5107 0 0 0 1 3 0 1 0
5108 1 0 0 1 2 0 0 0
5109 0 0 0 1 0 1 1 0

5110 rows × 8 columns

Matrice de corrélation¶

In [ ]:
# Tableau de corrélation
df_numeriques.corr()
# Matrice de corrélation
plt.figure(figsize=(12,8))
sns.heatmap(df.corr() , annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)
plt.xticks(rotation=45, ha='right')
Out[ ]:
(array([ 0.5,  1.5,  2.5,  3.5,  4.5,  5.5,  6.5,  7.5,  8.5,  9.5, 10.5]),
 [Text(0.5, 0, 'Genre'),
  Text(1.5, 0, 'Age'),
  Text(2.5, 0, 'Hypertension'),
  Text(3.5, 0, 'Maladie_Cardiaque'),
  Text(4.5, 0, 'Situation_Matrimoniale'),
  Text(5.5, 0, 'Type_travail'),
  Text(6.5, 0, 'Residence'),
  Text(7.5, 0, 'Taux_glucose_moyen'),
  Text(8.5, 0, 'IMC'),
  Text(9.5, 0, 'Statut_Fumer'),
  Text(10.5, 0, 'AVC')])
No description has been provided for this image
In [ ]:
correlation_values = abs(df.corr()['AVC'].drop('AVC')).sort_values(ascending=False)
correlation_values
Out[ ]:
AVC
Age 0.245257
Maladie_Cardiaque 0.134914
Hypertension 0.127904
Taux_glucose_moyen 0.115652
Situation_Matrimoniale 0.108340
IMC 0.045765
Statut_Fumer 0.037057
Type_travail 0.032316
Residence 0.015458
Genre 0.008929

Normalisation des variables numériques¶

In [ ]:
scaler = StandardScaler()
df_numeriques_normalise = pd.DataFrame(scaler.fit_transform(df_numeriques), columns=df_numeriques.columns)
df_numeriques_normalise
Out[ ]:
Age Taux_glucose_moyen IMC
0 1.051434 2.058363 1.106633
1 0.786070 2.058363 0.024107
2 1.626390 0.148256 0.530729
3 0.255342 2.058363 0.797611
4 1.582163 2.058363 -0.663218
... ... ... ...
5105 1.626390 -0.519284 0.024107
5106 1.670617 0.728778 1.584211
5107 -0.363842 -0.542168 0.263846
5108 0.343796 1.966000 -0.438475
5109 0.034205 -0.473216 -0.354197

5110 rows × 3 columns

Concaténation des deux bases (categorielle et numerique)¶

In [ ]:
df_final = pd.concat([df_numeriques_normalise,df_categorielles], axis=1)
df_final
Out[ ]:
Age Taux_glucose_moyen IMC Genre Hypertension Maladie_Cardiaque Situation_Matrimoniale Type_travail Residence Statut_Fumer AVC
0 1.051434 2.058363 1.106633 1 0 1 1 2 1 0 1
1 0.786070 2.058363 0.024107 0 0 0 1 3 0 1 1
2 1.626390 0.148256 0.530729 1 0 1 1 2 0 1 1
3 0.255342 2.058363 0.797611 0 0 0 1 2 1 2 1
4 1.582163 2.058363 -0.663218 0 1 0 1 3 0 1 1
... ... ... ... ... ... ... ... ... ... ... ...
5105 1.626390 -0.519284 0.024107 0 1 0 1 2 1 1 0
5106 1.670617 0.728778 1.584211 0 0 0 1 3 1 1 0
5107 -0.363842 -0.542168 0.263846 0 0 0 1 3 0 1 0
5108 0.343796 1.966000 -0.438475 1 0 0 1 2 0 0 0
5109 0.034205 -0.473216 -0.354197 0 0 0 1 0 1 1 0

5110 rows × 11 columns

In [ ]:
df_final.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5110 entries, 0 to 5109
Data columns (total 11 columns):
 #   Column                  Non-Null Count  Dtype   
---  ------                  --------------  -----   
 0   Age                     5110 non-null   float64 
 1   Taux_glucose_moyen      5110 non-null   float64 
 2   IMC                     5110 non-null   float64 
 3   Genre                   5110 non-null   category
 4   Hypertension            5110 non-null   category
 5   Maladie_Cardiaque       5110 non-null   category
 6   Situation_Matrimoniale  5110 non-null   category
 7   Type_travail            5110 non-null   category
 8   Residence               5110 non-null   category
 9   Statut_Fumer            5110 non-null   category
 10  AVC                     5110 non-null   category
dtypes: category(8), float64(3)
memory usage: 160.9 KB
In [ ]:
df['Hypertension'] = df['Hypertension'].astype('int')
df['Maladie_Cardiaque'] = df['Maladie_Cardiaque'].astype('int')
df['Genre'] = df['Genre'].astype('int')
df['Type_travail'] = df['Type_travail'].astype('int')
df['Situation_Matrimoniale'] = df['Situation_Matrimoniale'].astype('int')
df['Residence'] = df['Residence'].astype('int')
df['Statut_Fumer'] = df['Statut_Fumer'].astype('int')
df['AVC'] = df['AVC'].astype('int')
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5110 entries, 0 to 5109
Data columns (total 11 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   Genre                   5110 non-null   int64  
 1   Age                     5110 non-null   float64
 2   Hypertension            5110 non-null   int64  
 3   Maladie_Cardiaque       5110 non-null   int64  
 4   Situation_Matrimoniale  5110 non-null   int64  
 5   Type_travail            5110 non-null   int64  
 6   Residence               5110 non-null   int64  
 7   Taux_glucose_moyen      5110 non-null   float64
 8   IMC                     5110 non-null   float64
 9   Statut_Fumer            5110 non-null   int64  
 10  AVC                     5110 non-null   int64  
dtypes: float64(3), int64(8)
memory usage: 439.3 KB

Modélisation¶

Séparation des données¶

In [ ]:
X = df_final.drop(['AVC','Residence'], axis = 1)
Y = df_final['AVC']
In [ ]:
X['Genre'] = X['Genre'].astype('int')
X['Maladie_Cardiaque'] = X['Maladie_Cardiaque'].astype('int')
X['Hypertension'] = X['Hypertension'].astype('int')
X['Type_travail'] = X['Type_travail'].astype('int')
X['Situation_Matrimoniale'] = X['Situation_Matrimoniale'].astype('int')
#X['Residence'] = X['Residence'].astype('int')
X['Statut_Fumer'] = X['Statut_Fumer'].astype('int')
print(X.dtypes)

Age                       float64
Taux_glucose_moyen        float64
IMC                       float64
Genre                       int64
Hypertension                int64
Maladie_Cardiaque           int64
Situation_Matrimoniale      int64
Type_travail                int64
Statut_Fumer                int64
dtype: object

Oversampling avec SMOTE¶

In [ ]:
from imblearn.over_sampling import SMOTE
smote = SMOTE(k_neighbors=5, random_state=42)
X_equi, Y_equi = smote.fit_resample(X, Y)
print("dimension de X: ", X_equi.shape)
print("dimension de Y: ", Y_equi.shape)

dimension de X:  (9722, 9)
dimension de Y:  (9722,)
In [ ]:
plt.subplot(1, 2, 2)
plt.bar([0, 1], [np.sum(Y_equi == 0), np.sum(Y_equi == 1)], edgecolor='black')
plt.title("Distribution des classes après SMOTE")
plt.xlabel("Classe")
plt.ylabel("Fréquence")
plt.xticks([0, 1], ['oui', 'Non'])
Out[ ]:
([<matplotlib.axis.XTick at 0x7f26f77322d0>,
  <matplotlib.axis.XTick at 0x7f26fa5bed50>],
 [Text(0, 0, 'oui'), Text(1, 0, 'Non')])
No description has been provided for this image
In [ ]:
# Division de la base de données en train et test
X_train,X_test,Y_train,Y_test = train_test_split(X_equi, Y_equi, test_size=0.3, random_state=2)
In [ ]:
print(X_train.shape)
print(X_test.shape)
print(Y_train.shape)
print(Y_test.shape)

(6805, 9)
(2917, 9)
(6805,)
(2917,)

Apprentissage et application du modèle¶

Regression logistique¶

In [ ]:
modele_RL = LogisticRegression(max_iter=1000)

Entrainement du modèle¶

In [ ]:
modele_RL.fit(X_train,Y_train)
Out[ ]:
LogisticRegression(max_iter=1000)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LogisticRegression(max_iter=1000)

Prédiction¶

In [ ]:
# Prédiction sur la base test
best_Y_test_pred_RL = modele_RL.predict(X_test)
best_Y_test_pred_RL
Out[ ]:
array([1, 0, 0, ..., 0, 0, 0])
In [ ]:
# Prédiction sur la base train
best_Y_train_pred_RL = modele_RL.predict(X_train)
best_Y_train_pred_RL
Out[ ]:
array([1, 0, 1, ..., 1, 0, 1])
In [ ]:
# prediction en proba sur la base train
Y_pred_train_proba_RL = modele_RL.predict_proba(X_train)
Y_pred_train_proba_RL
Out[ ]:
array([[0.20608268, 0.79391732],
       [0.63717569, 0.36282431],
       [0.20006969, 0.79993031],
       ...,
       [0.1952449 , 0.8047551 ],
       [0.93209852, 0.06790148],
       [0.42528603, 0.57471397]])

Mesure de performance¶

Accuracy¶

In [ ]:
# Sur la base test
Acc_test_RL = accuracy_score(Y_test, best_Y_test_pred_RL) * 100
Acc_train_RL = accuracy_score(Y_train, best_Y_train_pred_RL) * 100
# Sur la base train
print("Accuracy sur train:", Acc_train_RL, "%")
print("Accuracy sur test:", Acc_test_RL, "%")

Accuracy sur train: 78.14842027920646 %
Accuracy sur test: 78.02536852931094 %

F1 score¶

In [ ]:
# Sur la base test et train
F1_score_test_RL = f1_score(Y_test, best_Y_test_pred_RL, average='weighted') * 100
F1_score_train_RL = f1_score(Y_train, best_Y_train_pred_RL, average='weighted') * 100
# Sur la base train
print("F1_score sur train:", F1_score_train_RL, "%")
print("F1_score sur test:", F1_score_test_RL, "%")

F1_score sur train: 78.1158804890842 %
F1_score sur test: 77.99992059321391 %

Précision¶

In [ ]:
# Sur la base test et train
precision_score_test_RL = precision_score(Y_test, best_Y_test_pred_RL, average='weighted') * 100
precision_score_train_RL = precision_score(Y_train, best_Y_train_pred_RL, average='weighted') * 100
# Sur la base train
print("Précision sur train:", precision_score_train_RL, "%")
print("Précision sur test:", precision_score_test_RL, "%")

Précision sur train: 78.30383775754254 %
Précision sur test: 78.18523891560424 %

Recall¶

In [ ]:
# Sur la base test et train
recall_score_test_RL = recall_score(Y_test, best_Y_test_pred_RL, average='weighted') * 100
recall_score_train_RL = recall_score(Y_train, best_Y_train_pred_RL, average='weighted') * 100
# Sur la base train
print("Recall sur train:", recall_score_train_RL, "%")
print("Recall sur test:", recall_score_test_RL, "%")

Recall sur train: 78.14842027920646 %
Recall sur test: 78.02536852931094 %

Matrice de confusion¶

In [ ]:
matrice_RL = confusion_matrix(Y_test, best_Y_test_pred_RL)
In [ ]:
from mlxtend.plotting import plot_confusion_matrix
# Affichage de la matrice de confusion avec titre
fig, ax = plot_confusion_matrix(conf_mat=matrice_RL,
                                show_absolute=True,
                                show_normed=True,
                                colorbar=True)

plt.title("Confusion Matrix RL")
plt.xlabel('Prédit')
plt.ylabel('Réel')
plt.show()

print("Alors dans la classe 0,sur",Y_test.value_counts()[0],
      "individu,le modèle réussit à faire un bon classement sur", matrice_RL[0,0],
      " individu et une erreur sur", matrice_RL[0,1], "\n  Dans la classe 1,sur",Y_test.value_counts()[1],
      "individus le modèle fait un bon classement sur", matrice_RL[1,1],
      " individu et une erreur sur", matrice_RL[0,0] )
No description has been provided for this image 
Alors dans la classe 0,sur 1466 individu,le modèle réussit à faire un bon classement sur 1092  individu et une erreur sur 374 
  Dans la classe 1,sur 1451 individus le modèle fait un bon classement sur 1184  individu et une erreur sur 1092

*# taux de bon et mauvais classement*

In [ ]:
print(f"Taux de bon classement (Classe 0 - Sans AVC): {matrice_RL[0, 0] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 0 - Sans AVC): {matrice_RL[0, 1] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de bon classement (Classe 1 - Avec AVC): {matrice_RL[1, 1] / Y_test.value_counts()[1] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 1 - Avec AVC): {matrice_RL[1, 0] / Y_test.value_counts()[1] * 100:.2f} %")

Taux de bon classement (Classe 0 - Sans AVC): 74.49 %
Taux de mauvais classement (Classe 0 - Sans AVC): 25.51 %
Taux de bon classement (Classe 1 - Avec AVC): 81.60 %
Taux de mauvais classement (Classe 1 - Avec AVC): 18.40 %
In [ ]:
# Courbe ROC
fpr_RL, tpr_RL, thresholds_RL = roc_curve(Y_test, modele_RL.predict_proba(X_test)[:, 1])
roc_auc_RL = auc(fpr_RL, tpr_RL)

plt.figure()
plt.plot(fpr_RL, tpr_RL, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc_RL)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('Taux de faux positif')
plt.ylabel('Taux de vrai positif')
plt.title('Courbe de  ROC pour Logistic Regression')
plt.legend(loc="lower right")
plt.show()
No description has been provided for this image
In [ ]:
from sklearn.inspection import permutation_importance

result = permutation_importance(modele_RL, X, Y, n_repeats=10)
sorted_idx = result.importances_mean.argsort()

plt.barh(X.columns[sorted_idx], result.importances_mean[sorted_idx])
plt.xlabel("Importance des caractéristiques")
plt.show()
No description has been provided for this image

Arbre de décision¶

Optimisation des parametres avec GridSearchCV¶

In [ ]:
# Définir le modèle et les paramètres
modele_AD = DecisionTreeClassifier()
param_grid = [{
    'max_features':[1,2,3,4,5,6,7],
             'max_depth':[1,2,3,4,5,6,7,8,9,10]
             }
]
# Créer l'objet GridSearchCV
modele_opt_AD = GridSearchCV(modele_AD, # modèle initialisé
                         param_grid, # grilles de parametre du modèle
                         cv=5, # cross--validation
                         verbose=15 #longueur
                         )
# Entrainement du modèle
modele_opt_AD.fit(X_train,Y_train)
# parametre optimaux
best_param = modele_opt_AD.best_params_
print("VOILA LES MEILLEURS PARAMÉTRES :",best_param)
# meilleur modèle
best_modele_AD = modele_opt_AD.best_estimator_
best_modele_AD

Fitting 5 folds for each of 70 candidates, totalling 350 fits
[CV 1/5; 1/70] START max_depth=1, max_features=1................................
[CV 1/5; 1/70] END .max_depth=1, max_features=1;, score=0.584 total time=   0.0s
[CV 2/5; 1/70] START max_depth=1, max_features=1................................
[CV 2/5; 1/70] END .max_depth=1, max_features=1;, score=0.522 total time=   0.0s
[CV 3/5; 1/70] START max_depth=1, max_features=1................................
[CV 3/5; 1/70] END .max_depth=1, max_features=1;, score=0.527 total time=   0.0s
[CV 4/5; 1/70] START max_depth=1, max_features=1................................
[CV 4/5; 1/70] END .max_depth=1, max_features=1;, score=0.573 total time=   0.0s
[CV 5/5; 1/70] START max_depth=1, max_features=1................................
[CV 5/5; 1/70] END .max_depth=1, max_features=1;, score=0.511 total time=   0.0s
[CV 1/5; 2/70] START max_depth=1, max_features=2................................
[CV 1/5; 2/70] END .max_depth=1, max_features=2;, score=0.582 total time=   0.0s
[CV 2/5; 2/70] START max_depth=1, max_features=2................................
[CV 2/5; 2/70] END .max_depth=1, max_features=2;, score=0.788 total time=   0.0s
[CV 3/5; 2/70] START max_depth=1, max_features=2................................
[CV 3/5; 2/70] END .max_depth=1, max_features=2;, score=0.565 total time=   0.0s
[CV 4/5; 2/70] START max_depth=1, max_features=2................................
[CV 4/5; 2/70] END .max_depth=1, max_features=2;, score=0.573 total time=   0.0s
[CV 5/5; 2/70] START max_depth=1, max_features=2................................
[CV 5/5; 2/70] END .max_depth=1, max_features=2;, score=0.597 total time=   0.0s
[CV 1/5; 3/70] START max_depth=1, max_features=3................................
[CV 1/5; 3/70] END .max_depth=1, max_features=3;, score=0.572 total time=   0.0s
[CV 2/5; 3/70] START max_depth=1, max_features=3................................
[CV 2/5; 3/70] END .max_depth=1, max_features=3;, score=0.583 total time=   0.0s
[CV 3/5; 3/70] START max_depth=1, max_features=3................................
[CV 3/5; 3/70] END .max_depth=1, max_features=3;, score=0.602 total time=   0.0s
[CV 4/5; 3/70] START max_depth=1, max_features=3................................
[CV 4/5; 3/70] END .max_depth=1, max_features=3;, score=0.583 total time=   0.0s
[CV 5/5; 3/70] START max_depth=1, max_features=3................................
[CV 5/5; 3/70] END .max_depth=1, max_features=3;, score=0.597 total time=   0.0s
[CV 1/5; 4/70] START max_depth=1, max_features=4................................
[CV 1/5; 4/70] END .max_depth=1, max_features=4;, score=0.592 total time=   0.0s
[CV 2/5; 4/70] START max_depth=1, max_features=4................................
[CV 2/5; 4/70] END .max_depth=1, max_features=4;, score=0.788 total time=   0.0s
[CV 3/5; 4/70] START max_depth=1, max_features=4................................
[CV 3/5; 4/70] END .max_depth=1, max_features=4;, score=0.780 total time=   0.0s
[CV 4/5; 4/70] START max_depth=1, max_features=4................................
[CV 4/5; 4/70] END .max_depth=1, max_features=4;, score=0.573 total time=   0.0s
[CV 5/5; 4/70] START max_depth=1, max_features=4................................
[CV 5/5; 4/70] END .max_depth=1, max_features=4;, score=0.769 total time=   0.0s
[CV 1/5; 5/70] START max_depth=1, max_features=5................................
[CV 1/5; 5/70] END .max_depth=1, max_features=5;, score=0.608 total time=   0.0s
[CV 2/5; 5/70] START max_depth=1, max_features=5................................
[CV 2/5; 5/70] END .max_depth=1, max_features=5;, score=0.572 total time=   0.0s
[CV 3/5; 5/70] START max_depth=1, max_features=5................................
[CV 3/5; 5/70] END .max_depth=1, max_features=5;, score=0.565 total time=   0.0s
[CV 4/5; 5/70] START max_depth=1, max_features=5................................
[CV 4/5; 5/70] END .max_depth=1, max_features=5;, score=0.790 total time=   0.0s
[CV 5/5; 5/70] START max_depth=1, max_features=5................................
[CV 5/5; 5/70] END .max_depth=1, max_features=5;, score=0.557 total time=   0.0s
[CV 1/5; 6/70] START max_depth=1, max_features=6................................
[CV 1/5; 6/70] END .max_depth=1, max_features=6;, score=0.592 total time=   0.0s
[CV 2/5; 6/70] START max_depth=1, max_features=6................................
[CV 2/5; 6/70] END .max_depth=1, max_features=6;, score=0.788 total time=   0.0s
[CV 3/5; 6/70] START max_depth=1, max_features=6................................
[CV 3/5; 6/70] END .max_depth=1, max_features=6;, score=0.780 total time=   0.0s
[CV 4/5; 6/70] START max_depth=1, max_features=6................................
[CV 4/5; 6/70] END .max_depth=1, max_features=6;, score=0.623 total time=   0.0s
[CV 5/5; 6/70] START max_depth=1, max_features=6................................
[CV 5/5; 6/70] END .max_depth=1, max_features=6;, score=0.557 total time=   0.0s
[CV 1/5; 7/70] START max_depth=1, max_features=7................................
[CV 1/5; 7/70] END .max_depth=1, max_features=7;, score=0.796 total time=   0.0s
[CV 2/5; 7/70] START max_depth=1, max_features=7................................
[CV 2/5; 7/70] END .max_depth=1, max_features=7;, score=0.572 total time=   0.0s
[CV 3/5; 7/70] START max_depth=1, max_features=7................................
[CV 3/5; 7/70] END .max_depth=1, max_features=7;, score=0.780 total time=   0.0s
[CV 4/5; 7/70] START max_depth=1, max_features=7................................
[CV 4/5; 7/70] END .max_depth=1, max_features=7;, score=0.790 total time=   0.0s
[CV 5/5; 7/70] START max_depth=1, max_features=7................................
[CV 5/5; 7/70] END .max_depth=1, max_features=7;, score=0.769 total time=   0.0s
[CV 1/5; 8/70] START max_depth=2, max_features=1................................
[CV 1/5; 8/70] END .max_depth=2, max_features=1;, score=0.563 total time=   0.0s
[CV 2/5; 8/70] START max_depth=2, max_features=1................................
[CV 2/5; 8/70] END .max_depth=2, max_features=1;, score=0.572 total time=   0.0s
[CV 3/5; 8/70] START max_depth=2, max_features=1................................
[CV 3/5; 8/70] END .max_depth=2, max_features=1;, score=0.616 total time=   0.0s
[CV 4/5; 8/70] START max_depth=2, max_features=1................................
[CV 4/5; 8/70] END .max_depth=2, max_features=1;, score=0.571 total time=   0.0s
[CV 5/5; 8/70] START max_depth=2, max_features=1................................
[CV 5/5; 8/70] END .max_depth=2, max_features=1;, score=0.586 total time=   0.0s
[CV 1/5; 9/70] START max_depth=2, max_features=2................................
[CV 1/5; 9/70] END .max_depth=2, max_features=2;, score=0.796 total time=   0.0s
[CV 2/5; 9/70] START max_depth=2, max_features=2................................
[CV 2/5; 9/70] END .max_depth=2, max_features=2;, score=0.664 total time=   0.0s
[CV 3/5; 9/70] START max_depth=2, max_features=2................................
[CV 3/5; 9/70] END .max_depth=2, max_features=2;, score=0.619 total time=   0.0s
[CV 4/5; 9/70] START max_depth=2, max_features=2................................
[CV 4/5; 9/70] END .max_depth=2, max_features=2;, score=0.790 total time=   0.0s
[CV 5/5; 9/70] START max_depth=2, max_features=2................................
[CV 5/5; 9/70] END .max_depth=2, max_features=2;, score=0.613 total time=   0.0s
[CV 1/5; 10/70] START max_depth=2, max_features=3...............................
[CV 1/5; 10/70] END max_depth=2, max_features=3;, score=0.796 total time=   0.0s
[CV 2/5; 10/70] START max_depth=2, max_features=3...............................
[CV 2/5; 10/70] END max_depth=2, max_features=3;, score=0.609 total time=   0.0s
[CV 3/5; 10/70] START max_depth=2, max_features=3...............................
[CV 3/5; 10/70] END max_depth=2, max_features=3;, score=0.780 total time=   0.0s
[CV 4/5; 10/70] START max_depth=2, max_features=3...............................
[CV 4/5; 10/70] END max_depth=2, max_features=3;, score=0.583 total time=   0.0s
[CV 5/5; 10/70] START max_depth=2, max_features=3...............................
[CV 5/5; 10/70] END max_depth=2, max_features=3;, score=0.623 total time=   0.0s
[CV 1/5; 11/70] START max_depth=2, max_features=4...............................
[CV 1/5; 11/70] END max_depth=2, max_features=4;, score=0.598 total time=   0.0s
[CV 2/5; 11/70] START max_depth=2, max_features=4...............................
[CV 2/5; 11/70] END max_depth=2, max_features=4;, score=0.791 total time=   0.0s
[CV 3/5; 11/70] START max_depth=2, max_features=4...............................
[CV 3/5; 11/70] END max_depth=2, max_features=4;, score=0.616 total time=   0.0s
[CV 4/5; 11/70] START max_depth=2, max_features=4...............................
[CV 4/5; 11/70] END max_depth=2, max_features=4;, score=0.790 total time=   0.0s
[CV 5/5; 11/70] START max_depth=2, max_features=4...............................
[CV 5/5; 11/70] END max_depth=2, max_features=4;, score=0.769 total time=   0.0s
[CV 1/5; 12/70] START max_depth=2, max_features=5...............................
[CV 1/5; 12/70] END max_depth=2, max_features=5;, score=0.760 total time=   0.0s
[CV 2/5; 12/70] START max_depth=2, max_features=5...............................
[CV 2/5; 12/70] END max_depth=2, max_features=5;, score=0.788 total time=   0.0s
[CV 3/5; 12/70] START max_depth=2, max_features=5...............................
[CV 3/5; 12/70] END max_depth=2, max_features=5;, score=0.750 total time=   0.0s
[CV 4/5; 12/70] START max_depth=2, max_features=5...............................
[CV 4/5; 12/70] END max_depth=2, max_features=5;, score=0.790 total time=   0.0s
[CV 5/5; 12/70] START max_depth=2, max_features=5...............................
[CV 5/5; 12/70] END max_depth=2, max_features=5;, score=0.769 total time=   0.0s
[CV 1/5; 13/70] START max_depth=2, max_features=6...............................
[CV 1/5; 13/70] END max_depth=2, max_features=6;, score=0.796 total time=   0.0s
[CV 2/5; 13/70] START max_depth=2, max_features=6...............................
[CV 2/5; 13/70] END max_depth=2, max_features=6;, score=0.788 total time=   0.0s
[CV 3/5; 13/70] START max_depth=2, max_features=6...............................
[CV 3/5; 13/70] END max_depth=2, max_features=6;, score=0.780 total time=   0.0s
[CV 4/5; 13/70] START max_depth=2, max_features=6...............................
[CV 4/5; 13/70] END max_depth=2, max_features=6;, score=0.790 total time=   0.0s
[CV 5/5; 13/70] START max_depth=2, max_features=6...............................
[CV 5/5; 13/70] END max_depth=2, max_features=6;, score=0.769 total time=   0.0s
[CV 1/5; 14/70] START max_depth=2, max_features=7...............................
[CV 1/5; 14/70] END max_depth=2, max_features=7;, score=0.796 total time=   0.0s
[CV 2/5; 14/70] START max_depth=2, max_features=7...............................
[CV 2/5; 14/70] END max_depth=2, max_features=7;, score=0.788 total time=   0.0s
[CV 3/5; 14/70] START max_depth=2, max_features=7...............................
[CV 3/5; 14/70] END max_depth=2, max_features=7;, score=0.773 total time=   0.0s
[CV 4/5; 14/70] START max_depth=2, max_features=7...............................
[CV 4/5; 14/70] END max_depth=2, max_features=7;, score=0.790 total time=   0.0s
[CV 5/5; 14/70] START max_depth=2, max_features=7...............................
[CV 5/5; 14/70] END max_depth=2, max_features=7;, score=0.769 total time=   0.0s
[CV 1/5; 15/70] START max_depth=3, max_features=1...............................
[CV 1/5; 15/70] END max_depth=3, max_features=1;, score=0.673 total time=   0.0s
[CV 2/5; 15/70] START max_depth=3, max_features=1...............................
[CV 2/5; 15/70] END max_depth=3, max_features=1;, score=0.788 total time=   0.0s
[CV 3/5; 15/70] START max_depth=3, max_features=1...............................
[CV 3/5; 15/70] END max_depth=3, max_features=1;, score=0.586 total time=   0.0s
[CV 4/5; 15/70] START max_depth=3, max_features=1...............................
[CV 4/5; 15/70] END max_depth=3, max_features=1;, score=0.598 total time=   0.0s
[CV 5/5; 15/70] START max_depth=3, max_features=1...............................
[CV 5/5; 15/70] END max_depth=3, max_features=1;, score=0.600 total time=   0.0s
[CV 1/5; 16/70] START max_depth=3, max_features=2...............................
[CV 1/5; 16/70] END max_depth=3, max_features=2;, score=0.796 total time=   0.0s
[CV 2/5; 16/70] START max_depth=3, max_features=2...............................
[CV 2/5; 16/70] END max_depth=3, max_features=2;, score=0.602 total time=   0.0s
[CV 3/5; 16/70] START max_depth=3, max_features=2...............................
[CV 3/5; 16/70] END max_depth=3, max_features=2;, score=0.617 total time=   0.0s
[CV 4/5; 16/70] START max_depth=3, max_features=2...............................
[CV 4/5; 16/70] END max_depth=3, max_features=2;, score=0.664 total time=   0.0s
[CV 5/5; 16/70] START max_depth=3, max_features=2...............................
[CV 5/5; 16/70] END max_depth=3, max_features=2;, score=0.728 total time=   0.0s
[CV 1/5; 17/70] START max_depth=3, max_features=3...............................
[CV 1/5; 17/70] END max_depth=3, max_features=3;, score=0.797 total time=   0.0s
[CV 2/5; 17/70] START max_depth=3, max_features=3...............................
[CV 2/5; 17/70] END max_depth=3, max_features=3;, score=0.743 total time=   0.0s
[CV 3/5; 17/70] START max_depth=3, max_features=3...............................
[CV 3/5; 17/70] END max_depth=3, max_features=3;, score=0.783 total time=   0.0s
[CV 4/5; 17/70] START max_depth=3, max_features=3...............................
[CV 4/5; 17/70] END max_depth=3, max_features=3;, score=0.792 total time=   0.0s
[CV 5/5; 17/70] START max_depth=3, max_features=3...............................
[CV 5/5; 17/70] END max_depth=3, max_features=3;, score=0.772 total time=   0.0s
[CV 1/5; 18/70] START max_depth=3, max_features=4...............................
[CV 1/5; 18/70] END max_depth=3, max_features=4;, score=0.802 total time=   0.0s
[CV 2/5; 18/70] START max_depth=3, max_features=4...............................
[CV 2/5; 18/70] END max_depth=3, max_features=4;, score=0.611 total time=   0.0s
[CV 3/5; 18/70] START max_depth=3, max_features=4...............................
[CV 3/5; 18/70] END max_depth=3, max_features=4;, score=0.780 total time=   0.0s
[CV 4/5; 18/70] START max_depth=3, max_features=4...............................
[CV 4/5; 18/70] END max_depth=3, max_features=4;, score=0.790 total time=   0.0s
[CV 5/5; 18/70] START max_depth=3, max_features=4...............................
[CV 5/5; 18/70] END max_depth=3, max_features=4;, score=0.771 total time=   0.0s
[CV 1/5; 19/70] START max_depth=3, max_features=5...............................
[CV 1/5; 19/70] END max_depth=3, max_features=5;, score=0.750 total time=   0.0s
[CV 2/5; 19/70] START max_depth=3, max_features=5...............................
[CV 2/5; 19/70] END max_depth=3, max_features=5;, score=0.788 total time=   0.0s
[CV 3/5; 19/70] START max_depth=3, max_features=5...............................
[CV 3/5; 19/70] END max_depth=3, max_features=5;, score=0.780 total time=   0.0s
[CV 4/5; 19/70] START max_depth=3, max_features=5...............................
[CV 4/5; 19/70] END max_depth=3, max_features=5;, score=0.789 total time=   0.0s
[CV 5/5; 19/70] START max_depth=3, max_features=5...............................
[CV 5/5; 19/70] END max_depth=3, max_features=5;, score=0.774 total time=   0.0s
[CV 1/5; 20/70] START max_depth=3, max_features=6...............................
[CV 1/5; 20/70] END max_depth=3, max_features=6;, score=0.796 total time=   0.0s
[CV 2/5; 20/70] START max_depth=3, max_features=6...............................
[CV 2/5; 20/70] END max_depth=3, max_features=6;, score=0.788 total time=   0.0s
[CV 3/5; 20/70] START max_depth=3, max_features=6...............................
[CV 3/5; 20/70] END max_depth=3, max_features=6;, score=0.780 total time=   0.0s
[CV 4/5; 20/70] START max_depth=3, max_features=6...............................
[CV 4/5; 20/70] END max_depth=3, max_features=6;, score=0.764 total time=   0.0s
[CV 5/5; 20/70] START max_depth=3, max_features=6...............................
[CV 5/5; 20/70] END max_depth=3, max_features=6;, score=0.774 total time=   0.0s
[CV 1/5; 21/70] START max_depth=3, max_features=7...............................
[CV 1/5; 21/70] END max_depth=3, max_features=7;, score=0.773 total time=   0.0s
[CV 2/5; 21/70] START max_depth=3, max_features=7...............................
[CV 2/5; 21/70] END max_depth=3, max_features=7;, score=0.794 total time=   0.0s
[CV 3/5; 21/70] START max_depth=3, max_features=7...............................
[CV 3/5; 21/70] END max_depth=3, max_features=7;, score=0.773 total time=   0.0s
[CV 4/5; 21/70] START max_depth=3, max_features=7...............................
[CV 4/5; 21/70] END max_depth=3, max_features=7;, score=0.796 total time=   0.0s
[CV 5/5; 21/70] START max_depth=3, max_features=7...............................
[CV 5/5; 21/70] END max_depth=3, max_features=7;, score=0.769 total time=   0.0s
[CV 1/5; 22/70] START max_depth=4, max_features=1...............................
[CV 1/5; 22/70] END max_depth=4, max_features=1;, score=0.688 total time=   0.0s
[CV 2/5; 22/70] START max_depth=4, max_features=1...............................
[CV 2/5; 22/70] END max_depth=4, max_features=1;, score=0.788 total time=   0.0s
[CV 3/5; 22/70] START max_depth=4, max_features=1...............................
[CV 3/5; 22/70] END max_depth=4, max_features=1;, score=0.774 total time=   0.0s
[CV 4/5; 22/70] START max_depth=4, max_features=1...............................
[CV 4/5; 22/70] END max_depth=4, max_features=1;, score=0.675 total time=   0.0s
[CV 5/5; 22/70] START max_depth=4, max_features=1...............................
[CV 5/5; 22/70] END max_depth=4, max_features=1;, score=0.730 total time=   0.0s
[CV 1/5; 23/70] START max_depth=4, max_features=2...............................
[CV 1/5; 23/70] END max_depth=4, max_features=2;, score=0.760 total time=   0.0s
[CV 2/5; 23/70] START max_depth=4, max_features=2...............................
[CV 2/5; 23/70] END max_depth=4, max_features=2;, score=0.641 total time=   0.0s
[CV 3/5; 23/70] START max_depth=4, max_features=2...............................
[CV 3/5; 23/70] END max_depth=4, max_features=2;, score=0.759 total time=   0.0s
[CV 4/5; 23/70] START max_depth=4, max_features=2...............................
[CV 4/5; 23/70] END max_depth=4, max_features=2;, score=0.792 total time=   0.0s
[CV 5/5; 23/70] START max_depth=4, max_features=2...............................
[CV 5/5; 23/70] END max_depth=4, max_features=2;, score=0.692 total time=   0.0s
[CV 1/5; 24/70] START max_depth=4, max_features=3...............................
[CV 1/5; 24/70] END max_depth=4, max_features=3;, score=0.803 total time=   0.0s
[CV 2/5; 24/70] START max_depth=4, max_features=3...............................
[CV 2/5; 24/70] END max_depth=4, max_features=3;, score=0.773 total time=   0.0s
[CV 3/5; 24/70] START max_depth=4, max_features=3...............................
[CV 3/5; 24/70] END max_depth=4, max_features=3;, score=0.693 total time=   0.0s
[CV 4/5; 24/70] START max_depth=4, max_features=3...............................
[CV 4/5; 24/70] END max_depth=4, max_features=3;, score=0.788 total time=   0.0s
[CV 5/5; 24/70] START max_depth=4, max_features=3...............................
[CV 5/5; 24/70] END max_depth=4, max_features=3;, score=0.770 total time=   0.0s
[CV 1/5; 25/70] START max_depth=4, max_features=4...............................
[CV 1/5; 25/70] END max_depth=4, max_features=4;, score=0.796 total time=   0.0s
[CV 2/5; 25/70] START max_depth=4, max_features=4...............................
[CV 2/5; 25/70] END max_depth=4, max_features=4;, score=0.790 total time=   0.0s
[CV 3/5; 25/70] START max_depth=4, max_features=4...............................
[CV 3/5; 25/70] END max_depth=4, max_features=4;, score=0.788 total time=   0.0s
[CV 4/5; 25/70] START max_depth=4, max_features=4...............................
[CV 4/5; 25/70] END max_depth=4, max_features=4;, score=0.743 total time=   0.0s
[CV 5/5; 25/70] START max_depth=4, max_features=4...............................
[CV 5/5; 25/70] END max_depth=4, max_features=4;, score=0.735 total time=   0.0s
[CV 1/5; 26/70] START max_depth=4, max_features=5...............................
[CV 1/5; 26/70] END max_depth=4, max_features=5;, score=0.794 total time=   0.0s
[CV 2/5; 26/70] START max_depth=4, max_features=5...............................
[CV 2/5; 26/70] END max_depth=4, max_features=5;, score=0.801 total time=   0.0s
[CV 3/5; 26/70] START max_depth=4, max_features=5...............................
[CV 3/5; 26/70] END max_depth=4, max_features=5;, score=0.788 total time=   0.0s
[CV 4/5; 26/70] START max_depth=4, max_features=5...............................
[CV 4/5; 26/70] END max_depth=4, max_features=5;, score=0.773 total time=   0.0s
[CV 5/5; 26/70] START max_depth=4, max_features=5...............................
[CV 5/5; 26/70] END max_depth=4, max_features=5;, score=0.769 total time=   0.0s
[CV 1/5; 27/70] START max_depth=4, max_features=6...............................
[CV 1/5; 27/70] END max_depth=4, max_features=6;, score=0.804 total time=   0.0s
[CV 2/5; 27/70] START max_depth=4, max_features=6...............................
[CV 2/5; 27/70] END max_depth=4, max_features=6;, score=0.788 total time=   0.0s
[CV 3/5; 27/70] START max_depth=4, max_features=6...............................
[CV 3/5; 27/70] END max_depth=4, max_features=6;, score=0.791 total time=   0.0s
[CV 4/5; 27/70] START max_depth=4, max_features=6...............................
[CV 4/5; 27/70] END max_depth=4, max_features=6;, score=0.789 total time=   0.0s
[CV 5/5; 27/70] START max_depth=4, max_features=6...............................
[CV 5/5; 27/70] END max_depth=4, max_features=6;, score=0.780 total time=   0.0s
[CV 1/5; 28/70] START max_depth=4, max_features=7...............................
[CV 1/5; 28/70] END max_depth=4, max_features=7;, score=0.786 total time=   0.0s
[CV 2/5; 28/70] START max_depth=4, max_features=7...............................
[CV 2/5; 28/70] END max_depth=4, max_features=7;, score=0.791 total time=   0.0s
[CV 3/5; 28/70] START max_depth=4, max_features=7...............................
[CV 3/5; 28/70] END max_depth=4, max_features=7;, score=0.780 total time=   0.0s
[CV 4/5; 28/70] START max_depth=4, max_features=7...............................
[CV 4/5; 28/70] END max_depth=4, max_features=7;, score=0.797 total time=   0.0s
[CV 5/5; 28/70] START max_depth=4, max_features=7...............................
[CV 5/5; 28/70] END max_depth=4, max_features=7;, score=0.773 total time=   0.0s
[CV 1/5; 29/70] START max_depth=5, max_features=1...............................
[CV 1/5; 29/70] END max_depth=5, max_features=1;, score=0.800 total time=   0.0s
[CV 2/5; 29/70] START max_depth=5, max_features=1...............................
[CV 2/5; 29/70] END max_depth=5, max_features=1;, score=0.765 total time=   0.0s
[CV 3/5; 29/70] START max_depth=5, max_features=1...............................
[CV 3/5; 29/70] END max_depth=5, max_features=1;, score=0.682 total time=   0.0s
[CV 4/5; 29/70] START max_depth=5, max_features=1...............................
[CV 4/5; 29/70] END max_depth=5, max_features=1;, score=0.664 total time=   0.0s
[CV 5/5; 29/70] START max_depth=5, max_features=1...............................
[CV 5/5; 29/70] END max_depth=5, max_features=1;, score=0.740 total time=   0.0s
[CV 1/5; 30/70] START max_depth=5, max_features=2...............................
[CV 1/5; 30/70] END max_depth=5, max_features=2;, score=0.816 total time=   0.0s
[CV 2/5; 30/70] START max_depth=5, max_features=2...............................
[CV 2/5; 30/70] END max_depth=5, max_features=2;, score=0.650 total time=   0.0s
[CV 3/5; 30/70] START max_depth=5, max_features=2...............................
[CV 3/5; 30/70] END max_depth=5, max_features=2;, score=0.739 total time=   0.0s
[CV 4/5; 30/70] START max_depth=5, max_features=2...............................
[CV 4/5; 30/70] END max_depth=5, max_features=2;, score=0.766 total time=   0.0s
[CV 5/5; 30/70] START max_depth=5, max_features=2...............................
[CV 5/5; 30/70] END max_depth=5, max_features=2;, score=0.771 total time=   0.0s
[CV 1/5; 31/70] START max_depth=5, max_features=3...............................
[CV 1/5; 31/70] END max_depth=5, max_features=3;, score=0.799 total time=   0.0s
[CV 2/5; 31/70] START max_depth=5, max_features=3...............................
[CV 2/5; 31/70] END max_depth=5, max_features=3;, score=0.798 total time=   0.0s
[CV 3/5; 31/70] START max_depth=5, max_features=3...............................
[CV 3/5; 31/70] END max_depth=5, max_features=3;, score=0.678 total time=   0.0s
[CV 4/5; 31/70] START max_depth=5, max_features=3...............................
[CV 4/5; 31/70] END max_depth=5, max_features=3;, score=0.800 total time=   0.0s
[CV 5/5; 31/70] START max_depth=5, max_features=3...............................
[CV 5/5; 31/70] END max_depth=5, max_features=3;, score=0.757 total time=   0.0s
[CV 1/5; 32/70] START max_depth=5, max_features=4...............................
[CV 1/5; 32/70] END max_depth=5, max_features=4;, score=0.801 total time=   0.0s
[CV 2/5; 32/70] START max_depth=5, max_features=4...............................
[CV 2/5; 32/70] END max_depth=5, max_features=4;, score=0.793 total time=   0.0s
[CV 3/5; 32/70] START max_depth=5, max_features=4...............................
[CV 3/5; 32/70] END max_depth=5, max_features=4;, score=0.782 total time=   0.0s
[CV 4/5; 32/70] START max_depth=5, max_features=4...............................
[CV 4/5; 32/70] END max_depth=5, max_features=4;, score=0.794 total time=   0.0s
[CV 5/5; 32/70] START max_depth=5, max_features=4...............................
[CV 5/5; 32/70] END max_depth=5, max_features=4;, score=0.785 total time=   0.0s
[CV 1/5; 33/70] START max_depth=5, max_features=5...............................
[CV 1/5; 33/70] END max_depth=5, max_features=5;, score=0.794 total time=   0.0s
[CV 2/5; 33/70] START max_depth=5, max_features=5...............................
[CV 2/5; 33/70] END max_depth=5, max_features=5;, score=0.789 total time=   0.0s
[CV 3/5; 33/70] START max_depth=5, max_features=5...............................
[CV 3/5; 33/70] END max_depth=5, max_features=5;, score=0.790 total time=   0.0s
[CV 4/5; 33/70] START max_depth=5, max_features=5...............................
[CV 4/5; 33/70] END max_depth=5, max_features=5;, score=0.801 total time=   0.0s
[CV 5/5; 33/70] START max_depth=5, max_features=5...............................
[CV 5/5; 33/70] END max_depth=5, max_features=5;, score=0.785 total time=   0.0s
[CV 1/5; 34/70] START max_depth=5, max_features=6...............................
[CV 1/5; 34/70] END max_depth=5, max_features=6;, score=0.800 total time=   0.0s
[CV 2/5; 34/70] START max_depth=5, max_features=6...............................
[CV 2/5; 34/70] END max_depth=5, max_features=6;, score=0.793 total time=   0.0s
[CV 3/5; 34/70] START max_depth=5, max_features=6...............................
[CV 3/5; 34/70] END max_depth=5, max_features=6;, score=0.798 total time=   0.0s
[CV 4/5; 34/70] START max_depth=5, max_features=6...............................
[CV 4/5; 34/70] END max_depth=5, max_features=6;, score=0.796 total time=   0.0s
[CV 5/5; 34/70] START max_depth=5, max_features=6...............................
[CV 5/5; 34/70] END max_depth=5, max_features=6;, score=0.776 total time=   0.0s
[CV 1/5; 35/70] START max_depth=5, max_features=7...............................
[CV 1/5; 35/70] END max_depth=5, max_features=7;, score=0.813 total time=   0.0s
[CV 2/5; 35/70] START max_depth=5, max_features=7...............................
[CV 2/5; 35/70] END max_depth=5, max_features=7;, score=0.805 total time=   0.0s
[CV 3/5; 35/70] START max_depth=5, max_features=7...............................
[CV 3/5; 35/70] END max_depth=5, max_features=7;, score=0.786 total time=   0.0s
[CV 4/5; 35/70] START max_depth=5, max_features=7...............................
[CV 4/5; 35/70] END max_depth=5, max_features=7;, score=0.807 total time=   0.0s
[CV 5/5; 35/70] START max_depth=5, max_features=7...............................
[CV 5/5; 35/70] END max_depth=5, max_features=7;, score=0.800 total time=   0.0s
[CV 1/5; 36/70] START max_depth=6, max_features=1...............................
[CV 1/5; 36/70] END max_depth=6, max_features=1;, score=0.780 total time=   0.0s
[CV 2/5; 36/70] START max_depth=6, max_features=1...............................
[CV 2/5; 36/70] END max_depth=6, max_features=1;, score=0.760 total time=   0.0s
[CV 3/5; 36/70] START max_depth=6, max_features=1...............................
[CV 3/5; 36/70] END max_depth=6, max_features=1;, score=0.785 total time=   0.0s
[CV 4/5; 36/70] START max_depth=6, max_features=1...............................
[CV 4/5; 36/70] END max_depth=6, max_features=1;, score=0.802 total time=   0.0s
[CV 5/5; 36/70] START max_depth=6, max_features=1...............................
[CV 5/5; 36/70] END max_depth=6, max_features=1;, score=0.688 total time=   0.0s
[CV 1/5; 37/70] START max_depth=6, max_features=2...............................
[CV 1/5; 37/70] END max_depth=6, max_features=2;, score=0.770 total time=   0.0s
[CV 2/5; 37/70] START max_depth=6, max_features=2...............................
[CV 2/5; 37/70] END max_depth=6, max_features=2;, score=0.796 total time=   0.0s
[CV 3/5; 37/70] START max_depth=6, max_features=2...............................
[CV 3/5; 37/70] END max_depth=6, max_features=2;, score=0.797 total time=   0.0s
[CV 4/5; 37/70] START max_depth=6, max_features=2...............................
[CV 4/5; 37/70] END max_depth=6, max_features=2;, score=0.792 total time=   0.0s
[CV 5/5; 37/70] START max_depth=6, max_features=2...............................
[CV 5/5; 37/70] END max_depth=6, max_features=2;, score=0.772 total time=   0.0s
[CV 1/5; 38/70] START max_depth=6, max_features=3...............................
[CV 1/5; 38/70] END max_depth=6, max_features=3;, score=0.814 total time=   0.0s
[CV 2/5; 38/70] START max_depth=6, max_features=3...............................
[CV 2/5; 38/70] END max_depth=6, max_features=3;, score=0.781 total time=   0.0s
[CV 3/5; 38/70] START max_depth=6, max_features=3...............................
[CV 3/5; 38/70] END max_depth=6, max_features=3;, score=0.733 total time=   0.0s
[CV 4/5; 38/70] START max_depth=6, max_features=3...............................
[CV 4/5; 38/70] END max_depth=6, max_features=3;, score=0.783 total time=   0.0s
[CV 5/5; 38/70] START max_depth=6, max_features=3...............................
[CV 5/5; 38/70] END max_depth=6, max_features=3;, score=0.784 total time=   0.0s
[CV 1/5; 39/70] START max_depth=6, max_features=4...............................
[CV 1/5; 39/70] END max_depth=6, max_features=4;, score=0.811 total time=   0.0s
[CV 2/5; 39/70] START max_depth=6, max_features=4...............................
[CV 2/5; 39/70] END max_depth=6, max_features=4;, score=0.805 total time=   0.0s
[CV 3/5; 39/70] START max_depth=6, max_features=4...............................
[CV 3/5; 39/70] END max_depth=6, max_features=4;, score=0.805 total time=   0.0s
[CV 4/5; 39/70] START max_depth=6, max_features=4...............................
[CV 4/5; 39/70] END max_depth=6, max_features=4;, score=0.827 total time=   0.0s
[CV 5/5; 39/70] START max_depth=6, max_features=4...............................
[CV 5/5; 39/70] END max_depth=6, max_features=4;, score=0.789 total time=   0.0s
[CV 1/5; 40/70] START max_depth=6, max_features=5...............................
[CV 1/5; 40/70] END max_depth=6, max_features=5;, score=0.819 total time=   0.0s
[CV 2/5; 40/70] START max_depth=6, max_features=5...............................
[CV 2/5; 40/70] END max_depth=6, max_features=5;, score=0.807 total time=   0.0s
[CV 3/5; 40/70] START max_depth=6, max_features=5...............................
[CV 3/5; 40/70] END max_depth=6, max_features=5;, score=0.789 total time=   0.0s
[CV 4/5; 40/70] START max_depth=6, max_features=5...............................
[CV 4/5; 40/70] END max_depth=6, max_features=5;, score=0.811 total time=   0.0s
[CV 5/5; 40/70] START max_depth=6, max_features=5...............................
[CV 5/5; 40/70] END max_depth=6, max_features=5;, score=0.792 total time=   0.0s
[CV 1/5; 41/70] START max_depth=6, max_features=6...............................
[CV 1/5; 41/70] END max_depth=6, max_features=6;, score=0.805 total time=   0.0s
[CV 2/5; 41/70] START max_depth=6, max_features=6...............................
[CV 2/5; 41/70] END max_depth=6, max_features=6;, score=0.822 total time=   0.0s
[CV 3/5; 41/70] START max_depth=6, max_features=6...............................
[CV 3/5; 41/70] END max_depth=6, max_features=6;, score=0.794 total time=   0.0s
[CV 4/5; 41/70] START max_depth=6, max_features=6...............................
[CV 4/5; 41/70] END max_depth=6, max_features=6;, score=0.810 total time=   0.0s
[CV 5/5; 41/70] START max_depth=6, max_features=6...............................
[CV 5/5; 41/70] END max_depth=6, max_features=6;, score=0.796 total time=   0.0s
[CV 1/5; 42/70] START max_depth=6, max_features=7...............................
[CV 1/5; 42/70] END max_depth=6, max_features=7;, score=0.803 total time=   0.0s
[CV 2/5; 42/70] START max_depth=6, max_features=7...............................
[CV 2/5; 42/70] END max_depth=6, max_features=7;, score=0.822 total time=   0.0s
[CV 3/5; 42/70] START max_depth=6, max_features=7...............................
[CV 3/5; 42/70] END max_depth=6, max_features=7;, score=0.812 total time=   0.0s
[CV 4/5; 42/70] START max_depth=6, max_features=7...............................
[CV 4/5; 42/70] END max_depth=6, max_features=7;, score=0.819 total time=   0.0s
[CV 5/5; 42/70] START max_depth=6, max_features=7...............................
[CV 5/5; 42/70] END max_depth=6, max_features=7;, score=0.796 total time=   0.0s
[CV 1/5; 43/70] START max_depth=7, max_features=1...............................
[CV 1/5; 43/70] END max_depth=7, max_features=1;, score=0.677 total time=   0.0s
[CV 2/5; 43/70] START max_depth=7, max_features=1...............................
[CV 2/5; 43/70] END max_depth=7, max_features=1;, score=0.762 total time=   0.0s
[CV 3/5; 43/70] START max_depth=7, max_features=1...............................
[CV 3/5; 43/70] END max_depth=7, max_features=1;, score=0.780 total time=   0.0s
[CV 4/5; 43/70] START max_depth=7, max_features=1...............................
[CV 4/5; 43/70] END max_depth=7, max_features=1;, score=0.783 total time=   0.0s
[CV 5/5; 43/70] START max_depth=7, max_features=1...............................
[CV 5/5; 43/70] END max_depth=7, max_features=1;, score=0.677 total time=   0.0s
[CV 1/5; 44/70] START max_depth=7, max_features=2...............................
[CV 1/5; 44/70] END max_depth=7, max_features=2;, score=0.812 total time=   0.0s
[CV 2/5; 44/70] START max_depth=7, max_features=2...............................
[CV 2/5; 44/70] END max_depth=7, max_features=2;, score=0.812 total time=   0.0s
[CV 3/5; 44/70] START max_depth=7, max_features=2...............................
[CV 3/5; 44/70] END max_depth=7, max_features=2;, score=0.804 total time=   0.0s
[CV 4/5; 44/70] START max_depth=7, max_features=2...............................
[CV 4/5; 44/70] END max_depth=7, max_features=2;, score=0.805 total time=   0.0s
[CV 5/5; 44/70] START max_depth=7, max_features=2...............................
[CV 5/5; 44/70] END max_depth=7, max_features=2;, score=0.790 total time=   0.0s
[CV 1/5; 45/70] START max_depth=7, max_features=3...............................
[CV 1/5; 45/70] END max_depth=7, max_features=3;, score=0.812 total time=   0.0s
[CV 2/5; 45/70] START max_depth=7, max_features=3...............................
[CV 2/5; 45/70] END max_depth=7, max_features=3;, score=0.784 total time=   0.0s
[CV 3/5; 45/70] START max_depth=7, max_features=3...............................
[CV 3/5; 45/70] END max_depth=7, max_features=3;, score=0.788 total time=   0.0s
[CV 4/5; 45/70] START max_depth=7, max_features=3...............................
[CV 4/5; 45/70] END max_depth=7, max_features=3;, score=0.802 total time=   0.0s
[CV 5/5; 45/70] START max_depth=7, max_features=3...............................
[CV 5/5; 45/70] END max_depth=7, max_features=3;, score=0.807 total time=   0.0s
[CV 1/5; 46/70] START max_depth=7, max_features=4...............................
[CV 1/5; 46/70] END max_depth=7, max_features=4;, score=0.806 total time=   0.0s
[CV 2/5; 46/70] START max_depth=7, max_features=4...............................
[CV 2/5; 46/70] END max_depth=7, max_features=4;, score=0.810 total time=   0.0s
[CV 3/5; 46/70] START max_depth=7, max_features=4...............................
[CV 3/5; 46/70] END max_depth=7, max_features=4;, score=0.811 total time=   0.0s
[CV 4/5; 46/70] START max_depth=7, max_features=4...............................
[CV 4/5; 46/70] END max_depth=7, max_features=4;, score=0.825 total time=   0.0s
[CV 5/5; 46/70] START max_depth=7, max_features=4...............................
[CV 5/5; 46/70] END max_depth=7, max_features=4;, score=0.808 total time=   0.0s
[CV 1/5; 47/70] START max_depth=7, max_features=5...............................
[CV 1/5; 47/70] END max_depth=7, max_features=5;, score=0.824 total time=   0.0s
[CV 2/5; 47/70] START max_depth=7, max_features=5...............................
[CV 2/5; 47/70] END max_depth=7, max_features=5;, score=0.822 total time=   0.0s
[CV 3/5; 47/70] START max_depth=7, max_features=5...............................
[CV 3/5; 47/70] END max_depth=7, max_features=5;, score=0.802 total time=   0.0s
[CV 4/5; 47/70] START max_depth=7, max_features=5...............................
[CV 4/5; 47/70] END max_depth=7, max_features=5;, score=0.818 total time=   0.0s
[CV 5/5; 47/70] START max_depth=7, max_features=5...............................
[CV 5/5; 47/70] END max_depth=7, max_features=5;, score=0.811 total time=   0.0s
[CV 1/5; 48/70] START max_depth=7, max_features=6...............................
[CV 1/5; 48/70] END max_depth=7, max_features=6;, score=0.827 total time=   0.0s
[CV 2/5; 48/70] START max_depth=7, max_features=6...............................
[CV 2/5; 48/70] END max_depth=7, max_features=6;, score=0.819 total time=   0.0s
[CV 3/5; 48/70] START max_depth=7, max_features=6...............................
[CV 3/5; 48/70] END max_depth=7, max_features=6;, score=0.824 total time=   0.0s
[CV 4/5; 48/70] START max_depth=7, max_features=6...............................
[CV 4/5; 48/70] END max_depth=7, max_features=6;, score=0.822 total time=   0.0s
[CV 5/5; 48/70] START max_depth=7, max_features=6...............................
[CV 5/5; 48/70] END max_depth=7, max_features=6;, score=0.816 total time=   0.0s
[CV 1/5; 49/70] START max_depth=7, max_features=7...............................
[CV 1/5; 49/70] END max_depth=7, max_features=7;, score=0.827 total time=   0.0s
[CV 2/5; 49/70] START max_depth=7, max_features=7...............................
[CV 2/5; 49/70] END max_depth=7, max_features=7;, score=0.815 total time=   0.0s
[CV 3/5; 49/70] START max_depth=7, max_features=7...............................
[CV 3/5; 49/70] END max_depth=7, max_features=7;, score=0.821 total time=   0.0s
[CV 4/5; 49/70] START max_depth=7, max_features=7...............................
[CV 4/5; 49/70] END max_depth=7, max_features=7;, score=0.835 total time=   0.0s
[CV 5/5; 49/70] START max_depth=7, max_features=7...............................
[CV 5/5; 49/70] END max_depth=7, max_features=7;, score=0.821 total time=   0.0s
[CV 1/5; 50/70] START max_depth=8, max_features=1...............................
[CV 1/5; 50/70] END max_depth=8, max_features=1;, score=0.732 total time=   0.0s
[CV 2/5; 50/70] START max_depth=8, max_features=1...............................
[CV 2/5; 50/70] END max_depth=8, max_features=1;, score=0.798 total time=   0.0s
[CV 3/5; 50/70] START max_depth=8, max_features=1...............................
[CV 3/5; 50/70] END max_depth=8, max_features=1;, score=0.799 total time=   0.0s
[CV 4/5; 50/70] START max_depth=8, max_features=1...............................
[CV 4/5; 50/70] END max_depth=8, max_features=1;, score=0.783 total time=   0.0s
[CV 5/5; 50/70] START max_depth=8, max_features=1...............................
[CV 5/5; 50/70] END max_depth=8, max_features=1;, score=0.782 total time=   0.0s
[CV 1/5; 51/70] START max_depth=8, max_features=2...............................
[CV 1/5; 51/70] END max_depth=8, max_features=2;, score=0.731 total time=   0.0s
[CV 2/5; 51/70] START max_depth=8, max_features=2...............................
[CV 2/5; 51/70] END max_depth=8, max_features=2;, score=0.812 total time=   0.0s
[CV 3/5; 51/70] START max_depth=8, max_features=2...............................
[CV 3/5; 51/70] END max_depth=8, max_features=2;, score=0.799 total time=   0.0s
[CV 4/5; 51/70] START max_depth=8, max_features=2...............................
[CV 4/5; 51/70] END max_depth=8, max_features=2;, score=0.790 total time=   0.0s
[CV 5/5; 51/70] START max_depth=8, max_features=2...............................
[CV 5/5; 51/70] END max_depth=8, max_features=2;, score=0.785 total time=   0.0s
[CV 1/5; 52/70] START max_depth=8, max_features=3...............................
[CV 1/5; 52/70] END max_depth=8, max_features=3;, score=0.817 total time=   0.0s
[CV 2/5; 52/70] START max_depth=8, max_features=3...............................
[CV 2/5; 52/70] END max_depth=8, max_features=3;, score=0.821 total time=   0.0s
[CV 3/5; 52/70] START max_depth=8, max_features=3...............................
[CV 3/5; 52/70] END max_depth=8, max_features=3;, score=0.799 total time=   0.0s
[CV 4/5; 52/70] START max_depth=8, max_features=3...............................
[CV 4/5; 52/70] END max_depth=8, max_features=3;, score=0.819 total time=   0.0s
[CV 5/5; 52/70] START max_depth=8, max_features=3...............................
[CV 5/5; 52/70] END max_depth=8, max_features=3;, score=0.779 total time=   0.0s
[CV 1/5; 53/70] START max_depth=8, max_features=4...............................
[CV 1/5; 53/70] END max_depth=8, max_features=4;, score=0.826 total time=   0.0s
[CV 2/5; 53/70] START max_depth=8, max_features=4...............................
[CV 2/5; 53/70] END max_depth=8, max_features=4;, score=0.830 total time=   0.0s
[CV 3/5; 53/70] START max_depth=8, max_features=4...............................
[CV 3/5; 53/70] END max_depth=8, max_features=4;, score=0.820 total time=   0.0s
[CV 4/5; 53/70] START max_depth=8, max_features=4...............................
[CV 4/5; 53/70] END max_depth=8, max_features=4;, score=0.807 total time=   0.0s
[CV 5/5; 53/70] START max_depth=8, max_features=4...............................
[CV 5/5; 53/70] END max_depth=8, max_features=4;, score=0.805 total time=   0.0s
[CV 1/5; 54/70] START max_depth=8, max_features=5...............................
[CV 1/5; 54/70] END max_depth=8, max_features=5;, score=0.828 total time=   0.0s
[CV 2/5; 54/70] START max_depth=8, max_features=5...............................
[CV 2/5; 54/70] END max_depth=8, max_features=5;, score=0.816 total time=   0.0s
[CV 3/5; 54/70] START max_depth=8, max_features=5...............................
[CV 3/5; 54/70] END max_depth=8, max_features=5;, score=0.820 total time=   0.0s
[CV 4/5; 54/70] START max_depth=8, max_features=5...............................
[CV 4/5; 54/70] END max_depth=8, max_features=5;, score=0.821 total time=   0.0s
[CV 5/5; 54/70] START max_depth=8, max_features=5...............................
[CV 5/5; 54/70] END max_depth=8, max_features=5;, score=0.821 total time=   0.0s
[CV 1/5; 55/70] START max_depth=8, max_features=6...............................
[CV 1/5; 55/70] END max_depth=8, max_features=6;, score=0.831 total time=   0.0s
[CV 2/5; 55/70] START max_depth=8, max_features=6...............................
[CV 2/5; 55/70] END max_depth=8, max_features=6;, score=0.819 total time=   0.0s
[CV 3/5; 55/70] START max_depth=8, max_features=6...............................
[CV 3/5; 55/70] END max_depth=8, max_features=6;, score=0.828 total time=   0.0s
[CV 4/5; 55/70] START max_depth=8, max_features=6...............................
[CV 4/5; 55/70] END max_depth=8, max_features=6;, score=0.830 total time=   0.0s
[CV 5/5; 55/70] START max_depth=8, max_features=6...............................
[CV 5/5; 55/70] END max_depth=8, max_features=6;, score=0.815 total time=   0.0s
[CV 1/5; 56/70] START max_depth=8, max_features=7...............................
[CV 1/5; 56/70] END max_depth=8, max_features=7;, score=0.821 total time=   0.0s
[CV 2/5; 56/70] START max_depth=8, max_features=7...............................
[CV 2/5; 56/70] END max_depth=8, max_features=7;, score=0.821 total time=   0.0s
[CV 3/5; 56/70] START max_depth=8, max_features=7...............................
[CV 3/5; 56/70] END max_depth=8, max_features=7;, score=0.821 total time=   0.0s
[CV 4/5; 56/70] START max_depth=8, max_features=7...............................
[CV 4/5; 56/70] END max_depth=8, max_features=7;, score=0.830 total time=   0.0s
[CV 5/5; 56/70] START max_depth=8, max_features=7...............................
[CV 5/5; 56/70] END max_depth=8, max_features=7;, score=0.826 total time=   0.0s
[CV 1/5; 57/70] START max_depth=9, max_features=1...............................
[CV 1/5; 57/70] END max_depth=9, max_features=1;, score=0.827 total time=   0.0s
[CV 2/5; 57/70] START max_depth=9, max_features=1...............................
[CV 2/5; 57/70] END max_depth=9, max_features=1;, score=0.776 total time=   0.0s
[CV 3/5; 57/70] START max_depth=9, max_features=1...............................
[CV 3/5; 57/70] END max_depth=9, max_features=1;, score=0.726 total time=   0.0s
[CV 4/5; 57/70] START max_depth=9, max_features=1...............................
[CV 4/5; 57/70] END max_depth=9, max_features=1;, score=0.802 total time=   0.0s
[CV 5/5; 57/70] START max_depth=9, max_features=1...............................
[CV 5/5; 57/70] END max_depth=9, max_features=1;, score=0.797 total time=   0.0s
[CV 1/5; 58/70] START max_depth=9, max_features=2...............................
[CV 1/5; 58/70] END max_depth=9, max_features=2;, score=0.799 total time=   0.0s
[CV 2/5; 58/70] START max_depth=9, max_features=2...............................
[CV 2/5; 58/70] END max_depth=9, max_features=2;, score=0.827 total time=   0.0s
[CV 3/5; 58/70] START max_depth=9, max_features=2...............................
[CV 3/5; 58/70] END max_depth=9, max_features=2;, score=0.802 total time=   0.0s
[CV 4/5; 58/70] START max_depth=9, max_features=2...............................
[CV 4/5; 58/70] END max_depth=9, max_features=2;, score=0.825 total time=   0.0s
[CV 5/5; 58/70] START max_depth=9, max_features=2...............................
[CV 5/5; 58/70] END max_depth=9, max_features=2;, score=0.826 total time=   0.0s
[CV 1/5; 59/70] START max_depth=9, max_features=3...............................
[CV 1/5; 59/70] END max_depth=9, max_features=3;, score=0.844 total time=   0.0s
[CV 2/5; 59/70] START max_depth=9, max_features=3...............................
[CV 2/5; 59/70] END max_depth=9, max_features=3;, score=0.820 total time=   0.0s
[CV 3/5; 59/70] START max_depth=9, max_features=3...............................
[CV 3/5; 59/70] END max_depth=9, max_features=3;, score=0.831 total time=   0.0s
[CV 4/5; 59/70] START max_depth=9, max_features=3...............................
[CV 4/5; 59/70] END max_depth=9, max_features=3;, score=0.816 total time=   0.0s
[CV 5/5; 59/70] START max_depth=9, max_features=3...............................
[CV 5/5; 59/70] END max_depth=9, max_features=3;, score=0.818 total time=   0.0s
[CV 1/5; 60/70] START max_depth=9, max_features=4...............................
[CV 1/5; 60/70] END max_depth=9, max_features=4;, score=0.823 total time=   0.0s
[CV 2/5; 60/70] START max_depth=9, max_features=4...............................
[CV 2/5; 60/70] END max_depth=9, max_features=4;, score=0.825 total time=   0.0s
[CV 3/5; 60/70] START max_depth=9, max_features=4...............................
[CV 3/5; 60/70] END max_depth=9, max_features=4;, score=0.825 total time=   0.0s
[CV 4/5; 60/70] START max_depth=9, max_features=4...............................
[CV 4/5; 60/70] END max_depth=9, max_features=4;, score=0.827 total time=   0.0s
[CV 5/5; 60/70] START max_depth=9, max_features=4...............................
[CV 5/5; 60/70] END max_depth=9, max_features=4;, score=0.830 total time=   0.0s
[CV 1/5; 61/70] START max_depth=9, max_features=5...............................
[CV 1/5; 61/70] END max_depth=9, max_features=5;, score=0.840 total time=   0.0s
[CV 2/5; 61/70] START max_depth=9, max_features=5...............................
[CV 2/5; 61/70] END max_depth=9, max_features=5;, score=0.833 total time=   0.0s
[CV 3/5; 61/70] START max_depth=9, max_features=5...............................
[CV 3/5; 61/70] END max_depth=9, max_features=5;, score=0.836 total time=   0.0s
[CV 4/5; 61/70] START max_depth=9, max_features=5...............................
[CV 4/5; 61/70] END max_depth=9, max_features=5;, score=0.838 total time=   0.0s
[CV 5/5; 61/70] START max_depth=9, max_features=5...............................
[CV 5/5; 61/70] END max_depth=9, max_features=5;, score=0.827 total time=   0.0s
[CV 1/5; 62/70] START max_depth=9, max_features=6...............................
[CV 1/5; 62/70] END max_depth=9, max_features=6;, score=0.829 total time=   0.0s
[CV 2/5; 62/70] START max_depth=9, max_features=6...............................
[CV 2/5; 62/70] END max_depth=9, max_features=6;, score=0.822 total time=   0.0s
[CV 3/5; 62/70] START max_depth=9, max_features=6...............................
[CV 3/5; 62/70] END max_depth=9, max_features=6;, score=0.846 total time=   0.0s
[CV 4/5; 62/70] START max_depth=9, max_features=6...............................
[CV 4/5; 62/70] END max_depth=9, max_features=6;, score=0.836 total time=   0.0s
[CV 5/5; 62/70] START max_depth=9, max_features=6...............................
[CV 5/5; 62/70] END max_depth=9, max_features=6;, score=0.829 total time=   0.0s
[CV 1/5; 63/70] START max_depth=9, max_features=7...............................
[CV 1/5; 63/70] END max_depth=9, max_features=7;, score=0.846 total time=   0.0s
[CV 2/5; 63/70] START max_depth=9, max_features=7...............................
[CV 2/5; 63/70] END max_depth=9, max_features=7;, score=0.832 total time=   0.0s
[CV 3/5; 63/70] START max_depth=9, max_features=7...............................
[CV 3/5; 63/70] END max_depth=9, max_features=7;, score=0.830 total time=   0.0s
[CV 4/5; 63/70] START max_depth=9, max_features=7...............................
[CV 4/5; 63/70] END max_depth=9, max_features=7;, score=0.849 total time=   0.0s
[CV 5/5; 63/70] START max_depth=9, max_features=7...............................
[CV 5/5; 63/70] END max_depth=9, max_features=7;, score=0.831 total time=   0.0s
[CV 1/5; 64/70] START max_depth=10, max_features=1..............................
[CV 1/5; 64/70] END max_depth=10, max_features=1;, score=0.805 total time=   0.0s
[CV 2/5; 64/70] START max_depth=10, max_features=1..............................
[CV 2/5; 64/70] END max_depth=10, max_features=1;, score=0.819 total time=   0.0s
[CV 3/5; 64/70] START max_depth=10, max_features=1..............................
[CV 3/5; 64/70] END max_depth=10, max_features=1;, score=0.832 total time=   0.0s
[CV 4/5; 64/70] START max_depth=10, max_features=1..............................
[CV 4/5; 64/70] END max_depth=10, max_features=1;, score=0.819 total time=   0.0s
[CV 5/5; 64/70] START max_depth=10, max_features=1..............................
[CV 5/5; 64/70] END max_depth=10, max_features=1;, score=0.799 total time=   0.0s
[CV 1/5; 65/70] START max_depth=10, max_features=2..............................
[CV 1/5; 65/70] END max_depth=10, max_features=2;, score=0.820 total time=   0.0s
[CV 2/5; 65/70] START max_depth=10, max_features=2..............................
[CV 2/5; 65/70] END max_depth=10, max_features=2;, score=0.825 total time=   0.0s
[CV 3/5; 65/70] START max_depth=10, max_features=2..............................
[CV 3/5; 65/70] END max_depth=10, max_features=2;, score=0.807 total time=   0.0s
[CV 4/5; 65/70] START max_depth=10, max_features=2..............................
[CV 4/5; 65/70] END max_depth=10, max_features=2;, score=0.821 total time=   0.0s
[CV 5/5; 65/70] START max_depth=10, max_features=2..............................
[CV 5/5; 65/70] END max_depth=10, max_features=2;, score=0.802 total time=   0.0s
[CV 1/5; 66/70] START max_depth=10, max_features=3..............................
[CV 1/5; 66/70] END max_depth=10, max_features=3;, score=0.838 total time=   0.0s
[CV 2/5; 66/70] START max_depth=10, max_features=3..............................
[CV 2/5; 66/70] END max_depth=10, max_features=3;, score=0.823 total time=   0.0s
[CV 3/5; 66/70] START max_depth=10, max_features=3..............................
[CV 3/5; 66/70] END max_depth=10, max_features=3;, score=0.844 total time=   0.0s
[CV 4/5; 66/70] START max_depth=10, max_features=3..............................
[CV 4/5; 66/70] END max_depth=10, max_features=3;, score=0.851 total time=   0.0s
[CV 5/5; 66/70] START max_depth=10, max_features=3..............................
[CV 5/5; 66/70] END max_depth=10, max_features=3;, score=0.832 total time=   0.0s
[CV 1/5; 67/70] START max_depth=10, max_features=4..............................
[CV 1/5; 67/70] END max_depth=10, max_features=4;, score=0.831 total time=   0.0s
[CV 2/5; 67/70] START max_depth=10, max_features=4..............................
[CV 2/5; 67/70] END max_depth=10, max_features=4;, score=0.832 total time=   0.0s
[CV 3/5; 67/70] START max_depth=10, max_features=4..............................
[CV 3/5; 67/70] END max_depth=10, max_features=4;, score=0.827 total time=   0.0s
[CV 4/5; 67/70] START max_depth=10, max_features=4..............................
[CV 4/5; 67/70] END max_depth=10, max_features=4;, score=0.852 total time=   0.0s
[CV 5/5; 67/70] START max_depth=10, max_features=4..............................
[CV 5/5; 67/70] END max_depth=10, max_features=4;, score=0.831 total time=   0.0s
[CV 1/5; 68/70] START max_depth=10, max_features=5..............................
[CV 1/5; 68/70] END max_depth=10, max_features=5;, score=0.847 total time=   0.0s
[CV 2/5; 68/70] START max_depth=10, max_features=5..............................
[CV 2/5; 68/70] END max_depth=10, max_features=5;, score=0.854 total time=   0.0s
[CV 3/5; 68/70] START max_depth=10, max_features=5..............................
[CV 3/5; 68/70] END max_depth=10, max_features=5;, score=0.826 total time=   0.0s
[CV 4/5; 68/70] START max_depth=10, max_features=5..............................
[CV 4/5; 68/70] END max_depth=10, max_features=5;, score=0.846 total time=   0.0s
[CV 5/5; 68/70] START max_depth=10, max_features=5..............................
[CV 5/5; 68/70] END max_depth=10, max_features=5;, score=0.811 total time=   0.0s
[CV 1/5; 69/70] START max_depth=10, max_features=6..............................
[CV 1/5; 69/70] END max_depth=10, max_features=6;, score=0.848 total time=   0.0s
[CV 2/5; 69/70] START max_depth=10, max_features=6..............................
[CV 2/5; 69/70] END max_depth=10, max_features=6;, score=0.846 total time=   0.0s
[CV 3/5; 69/70] START max_depth=10, max_features=6..............................
[CV 3/5; 69/70] END max_depth=10, max_features=6;, score=0.846 total time=   0.0s
[CV 4/5; 69/70] START max_depth=10, max_features=6..............................
[CV 4/5; 69/70] END max_depth=10, max_features=6;, score=0.846 total time=   0.0s
[CV 5/5; 69/70] START max_depth=10, max_features=6..............................
[CV 5/5; 69/70] END max_depth=10, max_features=6;, score=0.839 total time=   0.0s
[CV 1/5; 70/70] START max_depth=10, max_features=7..............................
[CV 1/5; 70/70] END max_depth=10, max_features=7;, score=0.835 total time=   0.0s
[CV 2/5; 70/70] START max_depth=10, max_features=7..............................
[CV 2/5; 70/70] END max_depth=10, max_features=7;, score=0.843 total time=   0.0s
[CV 3/5; 70/70] START max_depth=10, max_features=7..............................
[CV 3/5; 70/70] END max_depth=10, max_features=7;, score=0.846 total time=   0.0s
[CV 4/5; 70/70] START max_depth=10, max_features=7..............................
[CV 4/5; 70/70] END max_depth=10, max_features=7;, score=0.853 total time=   0.0s
[CV 5/5; 70/70] START max_depth=10, max_features=7..............................
[CV 5/5; 70/70] END max_depth=10, max_features=7;, score=0.836 total time=   0.0s
VOILA LES MEILLEURS PARAMÉTRES : {'max_depth': 10, 'max_features': 6}
Out[ ]:
DecisionTreeClassifier(max_depth=10, max_features=6)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeClassifier(max_depth=10, max_features=6)

Prédiction¶

In [ ]:
# Prédiction sur la base test
best_Y_test_pred_AD = best_modele_AD.predict(X_test)
best_Y_test_pred_AD
Out[ ]:
array([1, 0, 0, ..., 0, 0, 1])
In [ ]:
# Prédiction sur la base train
best_Y_train_pred_AD = best_modele_AD.predict(X_train)
best_Y_train_pred_AD
Out[ ]:
array([1, 1, 1, ..., 1, 0, 1])
In [ ]:
# prediction en proba sur la base train
Y_pred_train_proba_AD = best_modele_AD.predict_proba(X_train)
Y_pred_train_proba_AD
Out[ ]:
array([[0.09840675, 0.90159325],
       [0.3015873 , 0.6984127 ],
       [0.18726592, 0.81273408],
       ...,
       [0.09840675, 0.90159325],
       [1.        , 0.        ],
       [0.25827815, 0.74172185]])

Mesures de performance¶

Accuracy¶

In [ ]:
# Sur la base test
Acc_test_AD = accuracy_score(Y_test, best_Y_test_pred_AD) * 100
Acc_train_AD = accuracy_score(Y_train, best_Y_train_pred_AD) * 100
# Sur la base train
print("Accuracy sur train:", Acc_train_AD, "%")
print("Accuracy sur test:", Acc_test_AD, "%")

Accuracy sur train: 89.84570168993388 %
Accuracy sur test: 84.64175522797395 %

F1 score¶

In [ ]:
# Sur la base test et train
F1_score_test_AD = f1_score(Y_test, best_Y_test_pred_AD, average='weighted') * 100
F1_score_train_AD = f1_score(Y_train, best_Y_train_pred_AD, average='weighted') * 100
# Sur la base train
print("F1_score sur train:", F1_score_train_AD, "%")
print("F1_score sur test:", F1_score_test_AD, "%")

F1_score sur train: 89.77331870246697 %
F1_score sur test: 84.54707200462465 %

Précision¶

In [ ]:
# Sur la base test et train
precision_score_test_AD = precision_score(Y_test, best_Y_test_pred_AD, average='weighted') * 100
precision_score_train_AD = precision_score(Y_train, best_Y_train_pred_AD, average='weighted') * 100
# Sur la base train
print("Précision sur train:", precision_score_train_AD, "%")
print("Précision sur test:", precision_score_test_AD, "%")

Précision sur train: 90.97259595247407 %
Précision sur test: 85.58468624634192 %

Recall¶

In [ ]:
# Sur la base test et train
recall_score_test_AD = recall_score(Y_test, best_Y_test_pred_AD, average='weighted') * 100
recall_score_train_AD = recall_score(Y_train, best_Y_train_pred_AD, average='weighted') * 100
# Sur la base train
print("Recall sur train:", recall_score_train_AD, "%")
print("Recall sur test:", recall_score_test_AD, "%")

Recall sur train: 89.84570168993388 %
Recall sur test: 84.64175522797395 %

Matrice de confusion¶

In [ ]:
matrice_AD = confusion_matrix(Y_test, best_Y_test_pred_AD)
# Affichage de la matrice de confusion avec titre
fig, ax = plot_confusion_matrix(conf_mat=matrice_AD,
                                show_absolute=True,
                                show_normed=True,
                                colorbar=True)

plt.title("Confusion Matrix AD")
plt.show()
print("Alors dans la classe 0,sur",Y_test.value_counts()[0],
      "individu,le modèle réussit à faire un bon classement sur", matrice_AD[0,0],
      " individu et une erreur sur", matrice_AD[0,1], "\n  Dans la classe 1,sur",Y_test.value_counts()[1],
      "individus le modèle fait un bon classement sur", matrice_AD[1,1],
      " individu et une erreur sur", matrice_AD[0,0] )
No description has been provided for this image 
Alors dans la classe 0,sur 1466 individu,le modèle réussit à faire un bon classement sur 1124  individu et une erreur sur 342 
  Dans la classe 1,sur 1451 individus le modèle fait un bon classement sur 1345  individu et une erreur sur 1124

*taux de bon et mauvais classement*

In [ ]:
print(f"Taux de bon classement (Classe 0 - Sans AVC): {matrice_AD[0, 0] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 0 - Sans AVC): {matrice_AD[0, 1] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de bon classement (Classe 1 - Avec AVC): {matrice_AD[1, 1] / Y_test.value_counts()[1] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 1 - Avec AVC): {matrice_AD[1, 0] / Y_test.value_counts()[1] * 100:.2f} %")

Taux de bon classement (Classe 0 - Sans AVC): 76.67 %
Taux de mauvais classement (Classe 0 - Sans AVC): 23.33 %
Taux de bon classement (Classe 1 - Avec AVC): 92.69 %
Taux de mauvais classement (Classe 1 - Avec AVC): 7.31 %
In [ ]:
# Courbe ROC
fpr_AD, tpr_AD, thresholds_AD = roc_curve(Y_test, best_modele_AD.predict_proba(X_test)[:, 1])
roc_auc_AD = auc(fpr_AD, tpr_AD)

plt.figure()
plt.plot(fpr_AD, tpr_AD, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc_AD)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('Taux de faux positif')
plt.ylabel('Taux de vrai positif')
plt.title('Courbe de  ROC pour Arbre de decision')
plt.legend(loc="lower right")
plt.show()
No description has been provided for this image
In [ ]:
result = permutation_importance(best_modele_AD, X, Y, n_repeats=10)
sorted_idx = result.importances_mean.argsort()

plt.barh(X.columns[sorted_idx], result.importances_mean[sorted_idx])
plt.xlabel("Importance des caractéristiques")
plt.show()
No description has been provided for this image

Random Forest¶

In [ ]:
modele = RandomForestClassifier()

# Définir le modèle et les paramètres
modele_RF = RandomForestClassifier()
param_grid = [{
    'n_estimators':[1,2,3,4,5,6,7,8,9,10],
             'max_depth':[1,2,3,4,5,6,7,8,9,10],
              'min_samples_leaf':[1,2,3,4]
             }
]
# Créer l'objet GridSearchCV
modele_opt_RF = GridSearchCV(modele_RF, # modèle initialisé
                         param_grid, # grilles de parametre du modèle
                         cv=5, # cross--validation
                         verbose=15 #longueur
                            )
##Entrainement du modèle
modele_opt_RF.fit(X_train,Y_train)
# parametre optimaux
best_param = modele_opt_RF.best_params_
print("VOILA LES MEILLEURS PARAMÉTRES :",best_param)
# meilleur modèle
best_modele_RF = modele_opt_RF.best_estimator_
best_modele_RF

Fitting 5 folds for each of 400 candidates, totalling 2000 fits
[CV 1/5; 1/400] START max_depth=1, min_samples_leaf=1, n_estimators=1...........
[CV 1/5; 1/400] END max_depth=1, min_samples_leaf=1, n_estimators=1;, score=0.608 total time=   0.0s
[CV 2/5; 1/400] START max_depth=1, min_samples_leaf=1, n_estimators=1...........
[CV 2/5; 1/400] END max_depth=1, min_samples_leaf=1, n_estimators=1;, score=0.788 total time=   0.0s
[CV 3/5; 1/400] START max_depth=1, min_samples_leaf=1, n_estimators=1...........
[CV 3/5; 1/400] END max_depth=1, min_samples_leaf=1, n_estimators=1;, score=0.602 total time=   0.0s
[CV 4/5; 1/400] START max_depth=1, min_samples_leaf=1, n_estimators=1...........
[CV 4/5; 1/400] END max_depth=1, min_samples_leaf=1, n_estimators=1;, score=0.790 total time=   0.0s
[CV 5/5; 1/400] START max_depth=1, min_samples_leaf=1, n_estimators=1...........
[CV 5/5; 1/400] END max_depth=1, min_samples_leaf=1, n_estimators=1;, score=0.557 total time=   0.0s
[CV 1/5; 2/400] START max_depth=1, min_samples_leaf=1, n_estimators=2...........
[CV 1/5; 2/400] END max_depth=1, min_samples_leaf=1, n_estimators=2;, score=0.741 total time=   0.0s
[CV 2/5; 2/400] START max_depth=1, min_samples_leaf=1, n_estimators=2...........
[CV 2/5; 2/400] END max_depth=1, min_samples_leaf=1, n_estimators=2;, score=0.788 total time=   0.0s
[CV 3/5; 2/400] START max_depth=1, min_samples_leaf=1, n_estimators=2...........
[CV 3/5; 2/400] END max_depth=1, min_samples_leaf=1, n_estimators=2;, score=0.780 total time=   0.0s
[CV 4/5; 2/400] START max_depth=1, min_samples_leaf=1, n_estimators=2...........
[CV 4/5; 2/400] END max_depth=1, min_samples_leaf=1, n_estimators=2;, score=0.571 total time=   0.0s
[CV 5/5; 2/400] START max_depth=1, min_samples_leaf=1, n_estimators=2...........
[CV 5/5; 2/400] END max_depth=1, min_samples_leaf=1, n_estimators=2;, score=0.557 total time=   0.0s
[CV 1/5; 3/400] START max_depth=1, min_samples_leaf=1, n_estimators=3...........
[CV 1/5; 3/400] END max_depth=1, min_samples_leaf=1, n_estimators=3;, score=0.787 total time=   0.0s
[CV 2/5; 3/400] START max_depth=1, min_samples_leaf=1, n_estimators=3...........
[CV 2/5; 3/400] END max_depth=1, min_samples_leaf=1, n_estimators=3;, score=0.788 total time=   0.0s
[CV 3/5; 3/400] START max_depth=1, min_samples_leaf=1, n_estimators=3...........
[CV 3/5; 3/400] END max_depth=1, min_samples_leaf=1, n_estimators=3;, score=0.780 total time=   0.0s
[CV 4/5; 3/400] START max_depth=1, min_samples_leaf=1, n_estimators=3...........
[CV 4/5; 3/400] END max_depth=1, min_samples_leaf=1, n_estimators=3;, score=0.788 total time=   0.0s
[CV 5/5; 3/400] START max_depth=1, min_samples_leaf=1, n_estimators=3...........
[CV 5/5; 3/400] END max_depth=1, min_samples_leaf=1, n_estimators=3;, score=0.770 total time=   0.0s
[CV 1/5; 4/400] START max_depth=1, min_samples_leaf=1, n_estimators=4...........
[CV 1/5; 4/400] END max_depth=1, min_samples_leaf=1, n_estimators=4;, score=0.796 total time=   0.0s
[CV 2/5; 4/400] START max_depth=1, min_samples_leaf=1, n_estimators=4...........
[CV 2/5; 4/400] END max_depth=1, min_samples_leaf=1, n_estimators=4;, score=0.788 total time=   0.0s
[CV 3/5; 4/400] START max_depth=1, min_samples_leaf=1, n_estimators=4...........
[CV 3/5; 4/400] END max_depth=1, min_samples_leaf=1, n_estimators=4;, score=0.738 total time=   0.0s
[CV 4/5; 4/400] START max_depth=1, min_samples_leaf=1, n_estimators=4...........
[CV 4/5; 4/400] END max_depth=1, min_samples_leaf=1, n_estimators=4;, score=0.789 total time=   0.0s
[CV 5/5; 4/400] START max_depth=1, min_samples_leaf=1, n_estimators=4...........
[CV 5/5; 4/400] END max_depth=1, min_samples_leaf=1, n_estimators=4;, score=0.778 total time=   0.0s
[CV 1/5; 5/400] START max_depth=1, min_samples_leaf=1, n_estimators=5...........
[CV 1/5; 5/400] END max_depth=1, min_samples_leaf=1, n_estimators=5;, score=0.711 total time=   0.0s
[CV 2/5; 5/400] START max_depth=1, min_samples_leaf=1, n_estimators=5...........
[CV 2/5; 5/400] END max_depth=1, min_samples_leaf=1, n_estimators=5;, score=0.788 total time=   0.0s
[CV 3/5; 5/400] START max_depth=1, min_samples_leaf=1, n_estimators=5...........
[CV 3/5; 5/400] END max_depth=1, min_samples_leaf=1, n_estimators=5;, score=0.745 total time=   0.0s
[CV 4/5; 5/400] START max_depth=1, min_samples_leaf=1, n_estimators=5...........
[CV 4/5; 5/400] END max_depth=1, min_samples_leaf=1, n_estimators=5;, score=0.780 total time=   0.0s
[CV 5/5; 5/400] START max_depth=1, min_samples_leaf=1, n_estimators=5...........
[CV 5/5; 5/400] END max_depth=1, min_samples_leaf=1, n_estimators=5;, score=0.721 total time=   0.0s
[CV 1/5; 6/400] START max_depth=1, min_samples_leaf=1, n_estimators=6...........
[CV 1/5; 6/400] END max_depth=1, min_samples_leaf=1, n_estimators=6;, score=0.755 total time=   0.0s
[CV 2/5; 6/400] START max_depth=1, min_samples_leaf=1, n_estimators=6...........
[CV 2/5; 6/400] END max_depth=1, min_samples_leaf=1, n_estimators=6;, score=0.752 total time=   0.0s
[CV 3/5; 6/400] START max_depth=1, min_samples_leaf=1, n_estimators=6...........
[CV 3/5; 6/400] END max_depth=1, min_samples_leaf=1, n_estimators=6;, score=0.769 total time=   0.0s
[CV 4/5; 6/400] START max_depth=1, min_samples_leaf=1, n_estimators=6...........
[CV 4/5; 6/400] END max_depth=1, min_samples_leaf=1, n_estimators=6;, score=0.790 total time=   0.0s
[CV 5/5; 6/400] START max_depth=1, min_samples_leaf=1, n_estimators=6...........
[CV 5/5; 6/400] END max_depth=1, min_samples_leaf=1, n_estimators=6;, score=0.769 total time=   0.0s
[CV 1/5; 7/400] START max_depth=1, min_samples_leaf=1, n_estimators=7...........
[CV 1/5; 7/400] END max_depth=1, min_samples_leaf=1, n_estimators=7;, score=0.760 total time=   0.0s
[CV 2/5; 7/400] START max_depth=1, min_samples_leaf=1, n_estimators=7...........
[CV 2/5; 7/400] END max_depth=1, min_samples_leaf=1, n_estimators=7;, score=0.788 total time=   0.0s
[CV 3/5; 7/400] START max_depth=1, min_samples_leaf=1, n_estimators=7...........
[CV 3/5; 7/400] END max_depth=1, min_samples_leaf=1, n_estimators=7;, score=0.778 total time=   0.0s
[CV 4/5; 7/400] START max_depth=1, min_samples_leaf=1, n_estimators=7...........
[CV 4/5; 7/400] END max_depth=1, min_samples_leaf=1, n_estimators=7;, score=0.769 total time=   0.0s
[CV 5/5; 7/400] START max_depth=1, min_samples_leaf=1, n_estimators=7...........
[CV 5/5; 7/400] END max_depth=1, min_samples_leaf=1, n_estimators=7;, score=0.777 total time=   0.0s
[CV 1/5; 8/400] START max_depth=1, min_samples_leaf=1, n_estimators=8...........
[CV 1/5; 8/400] END max_depth=1, min_samples_leaf=1, n_estimators=8;, score=0.782 total time=   0.0s
[CV 2/5; 8/400] START max_depth=1, min_samples_leaf=1, n_estimators=8...........
[CV 2/5; 8/400] END max_depth=1, min_samples_leaf=1, n_estimators=8;, score=0.788 total time=   0.0s
[CV 3/5; 8/400] START max_depth=1, min_samples_leaf=1, n_estimators=8...........
[CV 3/5; 8/400] END max_depth=1, min_samples_leaf=1, n_estimators=8;, score=0.777 total time=   0.0s
[CV 4/5; 8/400] START max_depth=1, min_samples_leaf=1, n_estimators=8...........
[CV 4/5; 8/400] END max_depth=1, min_samples_leaf=1, n_estimators=8;, score=0.788 total time=   0.0s
[CV 5/5; 8/400] START max_depth=1, min_samples_leaf=1, n_estimators=8...........
[CV 5/5; 8/400] END max_depth=1, min_samples_leaf=1, n_estimators=8;, score=0.724 total time=   0.0s
[CV 1/5; 9/400] START max_depth=1, min_samples_leaf=1, n_estimators=9...........
[CV 1/5; 9/400] END max_depth=1, min_samples_leaf=1, n_estimators=9;, score=0.766 total time=   0.0s
[CV 2/5; 9/400] START max_depth=1, min_samples_leaf=1, n_estimators=9...........
[CV 2/5; 9/400] END max_depth=1, min_samples_leaf=1, n_estimators=9;, score=0.788 total time=   0.0s
[CV 3/5; 9/400] START max_depth=1, min_samples_leaf=1, n_estimators=9...........
[CV 3/5; 9/400] END max_depth=1, min_samples_leaf=1, n_estimators=9;, score=0.774 total time=   0.0s
[CV 4/5; 9/400] START max_depth=1, min_samples_leaf=1, n_estimators=9...........
[CV 4/5; 9/400] END max_depth=1, min_samples_leaf=1, n_estimators=9;, score=0.730 total time=   0.0s
[CV 5/5; 9/400] START max_depth=1, min_samples_leaf=1, n_estimators=9...........
[CV 5/5; 9/400] END max_depth=1, min_samples_leaf=1, n_estimators=9;, score=0.769 total time=   0.0s
[CV 1/5; 10/400] START max_depth=1, min_samples_leaf=1, n_estimators=10.........
[CV 1/5; 10/400] END max_depth=1, min_samples_leaf=1, n_estimators=10;, score=0.756 total time=   0.1s
[CV 2/5; 10/400] START max_depth=1, min_samples_leaf=1, n_estimators=10.........
[CV 2/5; 10/400] END max_depth=1, min_samples_leaf=1, n_estimators=10;, score=0.776 total time=   0.0s
[CV 3/5; 10/400] START max_depth=1, min_samples_leaf=1, n_estimators=10.........
[CV 3/5; 10/400] END max_depth=1, min_samples_leaf=1, n_estimators=10;, score=0.779 total time=   0.0s
[CV 4/5; 10/400] START max_depth=1, min_samples_leaf=1, n_estimators=10.........
[CV 4/5; 10/400] END max_depth=1, min_samples_leaf=1, n_estimators=10;, score=0.790 total time=   0.0s
[CV 5/5; 10/400] START max_depth=1, min_samples_leaf=1, n_estimators=10.........
[CV 5/5; 10/400] END max_depth=1, min_samples_leaf=1, n_estimators=10;, score=0.758 total time=   0.0s
[CV 1/5; 11/400] START max_depth=1, min_samples_leaf=2, n_estimators=1..........
[CV 1/5; 11/400] END max_depth=1, min_samples_leaf=2, n_estimators=1;, score=0.608 total time=   0.0s
[CV 2/5; 11/400] START max_depth=1, min_samples_leaf=2, n_estimators=1..........
[CV 2/5; 11/400] END max_depth=1, min_samples_leaf=2, n_estimators=1;, score=0.572 total time=   0.0s
[CV 3/5; 11/400] START max_depth=1, min_samples_leaf=2, n_estimators=1..........
[CV 3/5; 11/400] END max_depth=1, min_samples_leaf=2, n_estimators=1;, score=0.780 total time=   0.0s
[CV 4/5; 11/400] START max_depth=1, min_samples_leaf=2, n_estimators=1..........
[CV 4/5; 11/400] END max_depth=1, min_samples_leaf=2, n_estimators=1;, score=0.624 total time=   0.0s
[CV 5/5; 11/400] START max_depth=1, min_samples_leaf=2, n_estimators=1..........
[CV 5/5; 11/400] END max_depth=1, min_samples_leaf=2, n_estimators=1;, score=0.777 total time=   0.0s
[CV 1/5; 12/400] START max_depth=1, min_samples_leaf=2, n_estimators=2..........
[CV 1/5; 12/400] END max_depth=1, min_samples_leaf=2, n_estimators=2;, score=0.787 total time=   0.0s
[CV 2/5; 12/400] START max_depth=1, min_samples_leaf=2, n_estimators=2..........
[CV 2/5; 12/400] END max_depth=1, min_samples_leaf=2, n_estimators=2;, score=0.788 total time=   0.0s
[CV 3/5; 12/400] START max_depth=1, min_samples_leaf=2, n_estimators=2..........
[CV 3/5; 12/400] END max_depth=1, min_samples_leaf=2, n_estimators=2;, score=0.621 total time=   0.0s
[CV 4/5; 12/400] START max_depth=1, min_samples_leaf=2, n_estimators=2..........
[CV 4/5; 12/400] END max_depth=1, min_samples_leaf=2, n_estimators=2;, score=0.790 total time=   0.0s
[CV 5/5; 12/400] START max_depth=1, min_samples_leaf=2, n_estimators=2..........
[CV 5/5; 12/400] END max_depth=1, min_samples_leaf=2, n_estimators=2;, score=0.597 total time=   0.0s
[CV 1/5; 13/400] START max_depth=1, min_samples_leaf=2, n_estimators=3..........
[CV 1/5; 13/400] END max_depth=1, min_samples_leaf=2, n_estimators=3;, score=0.743 total time=   0.0s
[CV 2/5; 13/400] START max_depth=1, min_samples_leaf=2, n_estimators=3..........
[CV 2/5; 13/400] END max_depth=1, min_samples_leaf=2, n_estimators=3;, score=0.785 total time=   0.0s
[CV 3/5; 13/400] START max_depth=1, min_samples_leaf=2, n_estimators=3..........
[CV 3/5; 13/400] END max_depth=1, min_samples_leaf=2, n_estimators=3;, score=0.589 total time=   0.0s
[CV 4/5; 13/400] START max_depth=1, min_samples_leaf=2, n_estimators=3..........
[CV 4/5; 13/400] END max_depth=1, min_samples_leaf=2, n_estimators=3;, score=0.774 total time=   0.0s
[CV 5/5; 13/400] START max_depth=1, min_samples_leaf=2, n_estimators=3..........
[CV 5/5; 13/400] END max_depth=1, min_samples_leaf=2, n_estimators=3;, score=0.769 total time=   0.0s
[CV 1/5; 14/400] START max_depth=1, min_samples_leaf=2, n_estimators=4..........
[CV 1/5; 14/400] END max_depth=1, min_samples_leaf=2, n_estimators=4;, score=0.790 total time=   0.0s
[CV 2/5; 14/400] START max_depth=1, min_samples_leaf=2, n_estimators=4..........
[CV 2/5; 14/400] END max_depth=1, min_samples_leaf=2, n_estimators=4;, score=0.785 total time=   0.0s
[CV 3/5; 14/400] START max_depth=1, min_samples_leaf=2, n_estimators=4..........
[CV 3/5; 14/400] END max_depth=1, min_samples_leaf=2, n_estimators=4;, score=0.767 total time=   0.0s
[CV 4/5; 14/400] START max_depth=1, min_samples_leaf=2, n_estimators=4..........
[CV 4/5; 14/400] END max_depth=1, min_samples_leaf=2, n_estimators=4;, score=0.790 total time=   0.0s
[CV 5/5; 14/400] START max_depth=1, min_samples_leaf=2, n_estimators=4..........
[CV 5/5; 14/400] END max_depth=1, min_samples_leaf=2, n_estimators=4;, score=0.771 total time=   0.0s
[CV 1/5; 15/400] START max_depth=1, min_samples_leaf=2, n_estimators=5..........
[CV 1/5; 15/400] END max_depth=1, min_samples_leaf=2, n_estimators=5;, score=0.791 total time=   0.0s
[CV 2/5; 15/400] START max_depth=1, min_samples_leaf=2, n_estimators=5..........
[CV 2/5; 15/400] END max_depth=1, min_samples_leaf=2, n_estimators=5;, score=0.788 total time=   0.0s
[CV 3/5; 15/400] START max_depth=1, min_samples_leaf=2, n_estimators=5..........
[CV 3/5; 15/400] END max_depth=1, min_samples_leaf=2, n_estimators=5;, score=0.769 total time=   0.0s
[CV 4/5; 15/400] START max_depth=1, min_samples_leaf=2, n_estimators=5..........
[CV 4/5; 15/400] END max_depth=1, min_samples_leaf=2, n_estimators=5;, score=0.788 total time=   0.0s
[CV 5/5; 15/400] START max_depth=1, min_samples_leaf=2, n_estimators=5..........
[CV 5/5; 15/400] END max_depth=1, min_samples_leaf=2, n_estimators=5;, score=0.777 total time=   0.0s
[CV 1/5; 16/400] START max_depth=1, min_samples_leaf=2, n_estimators=6..........
[CV 1/5; 16/400] END max_depth=1, min_samples_leaf=2, n_estimators=6;, score=0.790 total time=   0.0s
[CV 2/5; 16/400] START max_depth=1, min_samples_leaf=2, n_estimators=6..........
[CV 2/5; 16/400] END max_depth=1, min_samples_leaf=2, n_estimators=6;, score=0.788 total time=   0.0s
[CV 3/5; 16/400] START max_depth=1, min_samples_leaf=2, n_estimators=6..........
[CV 3/5; 16/400] END max_depth=1, min_samples_leaf=2, n_estimators=6;, score=0.780 total time=   0.0s
[CV 4/5; 16/400] START max_depth=1, min_samples_leaf=2, n_estimators=6..........
[CV 4/5; 16/400] END max_depth=1, min_samples_leaf=2, n_estimators=6;, score=0.780 total time=   0.0s
[CV 5/5; 16/400] START max_depth=1, min_samples_leaf=2, n_estimators=6..........
[CV 5/5; 16/400] END max_depth=1, min_samples_leaf=2, n_estimators=6;, score=0.769 total time=   0.0s
[CV 1/5; 17/400] START max_depth=1, min_samples_leaf=2, n_estimators=7..........
[CV 1/5; 17/400] END max_depth=1, min_samples_leaf=2, n_estimators=7;, score=0.791 total time=   0.0s
[CV 2/5; 17/400] START max_depth=1, min_samples_leaf=2, n_estimators=7..........
[CV 2/5; 17/400] END max_depth=1, min_samples_leaf=2, n_estimators=7;, score=0.788 total time=   0.0s
[CV 3/5; 17/400] START max_depth=1, min_samples_leaf=2, n_estimators=7..........
[CV 3/5; 17/400] END max_depth=1, min_samples_leaf=2, n_estimators=7;, score=0.780 total time=   0.0s
[CV 4/5; 17/400] START max_depth=1, min_samples_leaf=2, n_estimators=7..........
[CV 4/5; 17/400] END max_depth=1, min_samples_leaf=2, n_estimators=7;, score=0.757 total time=   0.0s
[CV 5/5; 17/400] START max_depth=1, min_samples_leaf=2, n_estimators=7..........
[CV 5/5; 17/400] END max_depth=1, min_samples_leaf=2, n_estimators=7;, score=0.769 total time=   0.0s
[CV 1/5; 18/400] START max_depth=1, min_samples_leaf=2, n_estimators=8..........
[CV 1/5; 18/400] END max_depth=1, min_samples_leaf=2, n_estimators=8;, score=0.782 total time=   0.0s
[CV 2/5; 18/400] START max_depth=1, min_samples_leaf=2, n_estimators=8..........
[CV 2/5; 18/400] END max_depth=1, min_samples_leaf=2, n_estimators=8;, score=0.769 total time=   0.0s
[CV 3/5; 18/400] START max_depth=1, min_samples_leaf=2, n_estimators=8..........
[CV 3/5; 18/400] END max_depth=1, min_samples_leaf=2, n_estimators=8;, score=0.764 total time=   0.1s
[CV 4/5; 18/400] START max_depth=1, min_samples_leaf=2, n_estimators=8..........
[CV 4/5; 18/400] END max_depth=1, min_samples_leaf=2, n_estimators=8;, score=0.788 total time=   0.0s
[CV 5/5; 18/400] START max_depth=1, min_samples_leaf=2, n_estimators=8..........
[CV 5/5; 18/400] END max_depth=1, min_samples_leaf=2, n_estimators=8;, score=0.774 total time=   0.0s
[CV 1/5; 19/400] START max_depth=1, min_samples_leaf=2, n_estimators=9..........
[CV 1/5; 19/400] END max_depth=1, min_samples_leaf=2, n_estimators=9;, score=0.796 total time=   0.0s
[CV 2/5; 19/400] START max_depth=1, min_samples_leaf=2, n_estimators=9..........
[CV 2/5; 19/400] END max_depth=1, min_samples_leaf=2, n_estimators=9;, score=0.669 total time=   0.0s
[CV 3/5; 19/400] START max_depth=1, min_samples_leaf=2, n_estimators=9..........
[CV 3/5; 19/400] END max_depth=1, min_samples_leaf=2, n_estimators=9;, score=0.780 total time=   0.0s
[CV 4/5; 19/400] START max_depth=1, min_samples_leaf=2, n_estimators=9..........
[CV 4/5; 19/400] END max_depth=1, min_samples_leaf=2, n_estimators=9;, score=0.790 total time=   0.0s
[CV 5/5; 19/400] START max_depth=1, min_samples_leaf=2, n_estimators=9..........
[CV 5/5; 19/400] END max_depth=1, min_samples_leaf=2, n_estimators=9;, score=0.713 total time=   0.0s
[CV 1/5; 20/400] START max_depth=1, min_samples_leaf=2, n_estimators=10.........
[CV 1/5; 20/400] END max_depth=1, min_samples_leaf=2, n_estimators=10;, score=0.777 total time=   0.0s
[CV 2/5; 20/400] START max_depth=1, min_samples_leaf=2, n_estimators=10.........
[CV 2/5; 20/400] END max_depth=1, min_samples_leaf=2, n_estimators=10;, score=0.788 total time=   0.0s
[CV 3/5; 20/400] START max_depth=1, min_samples_leaf=2, n_estimators=10.........
[CV 3/5; 20/400] END max_depth=1, min_samples_leaf=2, n_estimators=10;, score=0.780 total time=   0.0s
[CV 4/5; 20/400] START max_depth=1, min_samples_leaf=2, n_estimators=10.........
[CV 4/5; 20/400] END max_depth=1, min_samples_leaf=2, n_estimators=10;, score=0.785 total time=   0.0s
[CV 5/5; 20/400] START max_depth=1, min_samples_leaf=2, n_estimators=10.........
[CV 5/5; 20/400] END max_depth=1, min_samples_leaf=2, n_estimators=10;, score=0.769 total time=   0.0s
[CV 1/5; 21/400] START max_depth=1, min_samples_leaf=3, n_estimators=1..........
[CV 1/5; 21/400] END max_depth=1, min_samples_leaf=3, n_estimators=1;, score=0.592 total time=   0.0s
[CV 2/5; 21/400] START max_depth=1, min_samples_leaf=3, n_estimators=1..........
[CV 2/5; 21/400] END max_depth=1, min_samples_leaf=3, n_estimators=1;, score=0.630 total time=   0.0s
[CV 3/5; 21/400] START max_depth=1, min_samples_leaf=3, n_estimators=1..........
[CV 3/5; 21/400] END max_depth=1, min_samples_leaf=3, n_estimators=1;, score=0.570 total time=   0.0s
[CV 4/5; 21/400] START max_depth=1, min_samples_leaf=3, n_estimators=1..........
[CV 4/5; 21/400] END max_depth=1, min_samples_leaf=3, n_estimators=1;, score=0.609 total time=   0.0s
[CV 5/5; 21/400] START max_depth=1, min_samples_leaf=3, n_estimators=1..........
[CV 5/5; 21/400] END max_depth=1, min_samples_leaf=3, n_estimators=1;, score=0.622 total time=   0.0s
[CV 1/5; 22/400] START max_depth=1, min_samples_leaf=3, n_estimators=2..........
[CV 1/5; 22/400] END max_depth=1, min_samples_leaf=3, n_estimators=2;, score=0.774 total time=   0.0s
[CV 2/5; 22/400] START max_depth=1, min_samples_leaf=3, n_estimators=2..........
[CV 2/5; 22/400] END max_depth=1, min_samples_leaf=3, n_estimators=2;, score=0.788 total time=   0.0s
[CV 3/5; 22/400] START max_depth=1, min_samples_leaf=3, n_estimators=2..........
[CV 3/5; 22/400] END max_depth=1, min_samples_leaf=3, n_estimators=2;, score=0.780 total time=   0.0s
[CV 4/5; 22/400] START max_depth=1, min_samples_leaf=3, n_estimators=2..........
[CV 4/5; 22/400] END max_depth=1, min_samples_leaf=3, n_estimators=2;, score=0.790 total time=   0.0s
[CV 5/5; 22/400] START max_depth=1, min_samples_leaf=3, n_estimators=2..........
[CV 5/5; 22/400] END max_depth=1, min_samples_leaf=3, n_estimators=2;, score=0.763 total time=   0.0s
[CV 1/5; 23/400] START max_depth=1, min_samples_leaf=3, n_estimators=3..........
[CV 1/5; 23/400] END max_depth=1, min_samples_leaf=3, n_estimators=3;, score=0.592 total time=   0.0s
[CV 2/5; 23/400] START max_depth=1, min_samples_leaf=3, n_estimators=3..........
[CV 2/5; 23/400] END max_depth=1, min_samples_leaf=3, n_estimators=3;, score=0.788 total time=   0.0s
[CV 3/5; 23/400] START max_depth=1, min_samples_leaf=3, n_estimators=3..........
[CV 3/5; 23/400] END max_depth=1, min_samples_leaf=3, n_estimators=3;, score=0.766 total time=   0.0s
[CV 4/5; 23/400] START max_depth=1, min_samples_leaf=3, n_estimators=3..........
[CV 4/5; 23/400] END max_depth=1, min_samples_leaf=3, n_estimators=3;, score=0.790 total time=   0.0s
[CV 5/5; 23/400] START max_depth=1, min_samples_leaf=3, n_estimators=3..........
[CV 5/5; 23/400] END max_depth=1, min_samples_leaf=3, n_estimators=3;, score=0.551 total time=   0.0s
[CV 1/5; 24/400] START max_depth=1, min_samples_leaf=3, n_estimators=4..........
[CV 1/5; 24/400] END max_depth=1, min_samples_leaf=3, n_estimators=4;, score=0.617 total time=   0.0s
[CV 2/5; 24/400] START max_depth=1, min_samples_leaf=3, n_estimators=4..........
[CV 2/5; 24/400] END max_depth=1, min_samples_leaf=3, n_estimators=4;, score=0.611 total time=   0.0s
[CV 3/5; 24/400] START max_depth=1, min_samples_leaf=3, n_estimators=4..........
[CV 3/5; 24/400] END max_depth=1, min_samples_leaf=3, n_estimators=4;, score=0.778 total time=   0.0s
[CV 4/5; 24/400] START max_depth=1, min_samples_leaf=3, n_estimators=4..........
[CV 4/5; 24/400] END max_depth=1, min_samples_leaf=3, n_estimators=4;, score=0.790 total time=   0.0s
[CV 5/5; 24/400] START max_depth=1, min_samples_leaf=3, n_estimators=4..........
[CV 5/5; 24/400] END max_depth=1, min_samples_leaf=3, n_estimators=4;, score=0.717 total time=   0.0s
[CV 1/5; 25/400] START max_depth=1, min_samples_leaf=3, n_estimators=5..........
[CV 1/5; 25/400] END max_depth=1, min_samples_leaf=3, n_estimators=5;, score=0.746 total time=   0.0s
[CV 2/5; 25/400] START max_depth=1, min_samples_leaf=3, n_estimators=5..........
[CV 2/5; 25/400] END max_depth=1, min_samples_leaf=3, n_estimators=5;, score=0.754 total time=   0.0s
[CV 3/5; 25/400] START max_depth=1, min_samples_leaf=3, n_estimators=5..........
[CV 3/5; 25/400] END max_depth=1, min_samples_leaf=3, n_estimators=5;, score=0.780 total time=   0.0s
[CV 4/5; 25/400] START max_depth=1, min_samples_leaf=3, n_estimators=5..........
[CV 4/5; 25/400] END max_depth=1, min_samples_leaf=3, n_estimators=5;, score=0.790 total time=   0.0s
[CV 5/5; 25/400] START max_depth=1, min_samples_leaf=3, n_estimators=5..........
[CV 5/5; 25/400] END max_depth=1, min_samples_leaf=3, n_estimators=5;, score=0.777 total time=   0.0s
[CV 1/5; 26/400] START max_depth=1, min_samples_leaf=3, n_estimators=6..........
[CV 1/5; 26/400] END max_depth=1, min_samples_leaf=3, n_estimators=6;, score=0.783 total time=   0.0s
[CV 2/5; 26/400] START max_depth=1, min_samples_leaf=3, n_estimators=6..........
[CV 2/5; 26/400] END max_depth=1, min_samples_leaf=3, n_estimators=6;, score=0.778 total time=   0.0s
[CV 3/5; 26/400] START max_depth=1, min_samples_leaf=3, n_estimators=6..........
[CV 3/5; 26/400] END max_depth=1, min_samples_leaf=3, n_estimators=6;, score=0.779 total time=   0.0s
[CV 4/5; 26/400] START max_depth=1, min_samples_leaf=3, n_estimators=6..........
[CV 4/5; 26/400] END max_depth=1, min_samples_leaf=3, n_estimators=6;, score=0.736 total time=   0.0s
[CV 5/5; 26/400] START max_depth=1, min_samples_leaf=3, n_estimators=6..........
[CV 5/5; 26/400] END max_depth=1, min_samples_leaf=3, n_estimators=6;, score=0.777 total time=   0.0s
[CV 1/5; 27/400] START max_depth=1, min_samples_leaf=3, n_estimators=7..........
[CV 1/5; 27/400] END max_depth=1, min_samples_leaf=3, n_estimators=7;, score=0.787 total time=   0.0s
[CV 2/5; 27/400] START max_depth=1, min_samples_leaf=3, n_estimators=7..........
[CV 2/5; 27/400] END max_depth=1, min_samples_leaf=3, n_estimators=7;, score=0.633 total time=   0.0s
[CV 3/5; 27/400] START max_depth=1, min_samples_leaf=3, n_estimators=7..........
[CV 3/5; 27/400] END max_depth=1, min_samples_leaf=3, n_estimators=7;, score=0.760 total time=   0.0s
[CV 4/5; 27/400] START max_depth=1, min_samples_leaf=3, n_estimators=7..........
[CV 4/5; 27/400] END max_depth=1, min_samples_leaf=3, n_estimators=7;, score=0.774 total time=   0.0s
[CV 5/5; 27/400] START max_depth=1, min_samples_leaf=3, n_estimators=7..........
[CV 5/5; 27/400] END max_depth=1, min_samples_leaf=3, n_estimators=7;, score=0.767 total time=   0.0s
[CV 1/5; 28/400] START max_depth=1, min_samples_leaf=3, n_estimators=8..........
[CV 1/5; 28/400] END max_depth=1, min_samples_leaf=3, n_estimators=8;, score=0.787 total time=   0.0s
[CV 2/5; 28/400] START max_depth=1, min_samples_leaf=3, n_estimators=8..........
[CV 2/5; 28/400] END max_depth=1, min_samples_leaf=3, n_estimators=8;, score=0.788 total time=   0.0s
[CV 3/5; 28/400] START max_depth=1, min_samples_leaf=3, n_estimators=8..........
[CV 3/5; 28/400] END max_depth=1, min_samples_leaf=3, n_estimators=8;, score=0.767 total time=   0.0s
[CV 4/5; 28/400] START max_depth=1, min_samples_leaf=3, n_estimators=8..........
[CV 4/5; 28/400] END max_depth=1, min_samples_leaf=3, n_estimators=8;, score=0.755 total time=   0.0s
[CV 5/5; 28/400] START max_depth=1, min_samples_leaf=3, n_estimators=8..........
[CV 5/5; 28/400] END max_depth=1, min_samples_leaf=3, n_estimators=8;, score=0.774 total time=   0.0s
[CV 1/5; 29/400] START max_depth=1, min_samples_leaf=3, n_estimators=9..........
[CV 1/5; 29/400] END max_depth=1, min_samples_leaf=3, n_estimators=9;, score=0.787 total time=   0.0s
[CV 2/5; 29/400] START max_depth=1, min_samples_leaf=3, n_estimators=9..........
[CV 2/5; 29/400] END max_depth=1, min_samples_leaf=3, n_estimators=9;, score=0.788 total time=   0.0s
[CV 3/5; 29/400] START max_depth=1, min_samples_leaf=3, n_estimators=9..........
[CV 3/5; 29/400] END max_depth=1, min_samples_leaf=3, n_estimators=9;, score=0.780 total time=   0.0s
[CV 4/5; 29/400] START max_depth=1, min_samples_leaf=3, n_estimators=9..........
[CV 4/5; 29/400] END max_depth=1, min_samples_leaf=3, n_estimators=9;, score=0.790 total time=   0.0s
[CV 5/5; 29/400] START max_depth=1, min_samples_leaf=3, n_estimators=9..........
[CV 5/5; 29/400] END max_depth=1, min_samples_leaf=3, n_estimators=9;, score=0.721 total time=   0.0s
[CV 1/5; 30/400] START max_depth=1, min_samples_leaf=3, n_estimators=10.........
[CV 1/5; 30/400] END max_depth=1, min_samples_leaf=3, n_estimators=10;, score=0.792 total time=   0.0s
[CV 2/5; 30/400] START max_depth=1, min_samples_leaf=3, n_estimators=10.........
[CV 2/5; 30/400] END max_depth=1, min_samples_leaf=3, n_estimators=10;, score=0.643 total time=   0.0s
[CV 3/5; 30/400] START max_depth=1, min_samples_leaf=3, n_estimators=10.........
[CV 3/5; 30/400] END max_depth=1, min_samples_leaf=3, n_estimators=10;, score=0.780 total time=   0.0s
[CV 4/5; 30/400] START max_depth=1, min_samples_leaf=3, n_estimators=10.........
[CV 4/5; 30/400] END max_depth=1, min_samples_leaf=3, n_estimators=10;, score=0.790 total time=   0.0s
[CV 5/5; 30/400] START max_depth=1, min_samples_leaf=3, n_estimators=10.........
[CV 5/5; 30/400] END max_depth=1, min_samples_leaf=3, n_estimators=10;, score=0.707 total time=   0.0s
[CV 1/5; 31/400] START max_depth=1, min_samples_leaf=4, n_estimators=1..........
[CV 1/5; 31/400] END max_depth=1, min_samples_leaf=4, n_estimators=1;, score=0.533 total time=   0.0s
[CV 2/5; 31/400] START max_depth=1, min_samples_leaf=4, n_estimators=1..........
[CV 2/5; 31/400] END max_depth=1, min_samples_leaf=4, n_estimators=1;, score=0.788 total time=   0.0s
[CV 3/5; 31/400] START max_depth=1, min_samples_leaf=4, n_estimators=1..........
[CV 3/5; 31/400] END max_depth=1, min_samples_leaf=4, n_estimators=1;, score=0.602 total time=   0.0s
[CV 4/5; 31/400] START max_depth=1, min_samples_leaf=4, n_estimators=1..........
[CV 4/5; 31/400] END max_depth=1, min_samples_leaf=4, n_estimators=1;, score=0.571 total time=   0.0s
[CV 5/5; 31/400] START max_depth=1, min_samples_leaf=4, n_estimators=1..........
[CV 5/5; 31/400] END max_depth=1, min_samples_leaf=4, n_estimators=1;, score=0.769 total time=   0.0s
[CV 1/5; 32/400] START max_depth=1, min_samples_leaf=4, n_estimators=2..........
[CV 1/5; 32/400] END max_depth=1, min_samples_leaf=4, n_estimators=2;, score=0.796 total time=   0.0s
[CV 2/5; 32/400] START max_depth=1, min_samples_leaf=4, n_estimators=2..........
[CV 2/5; 32/400] END max_depth=1, min_samples_leaf=4, n_estimators=2;, score=0.630 total time=   0.0s
[CV 3/5; 32/400] START max_depth=1, min_samples_leaf=4, n_estimators=2..........
[CV 3/5; 32/400] END max_depth=1, min_samples_leaf=4, n_estimators=2;, score=0.780 total time=   0.0s
[CV 4/5; 32/400] START max_depth=1, min_samples_leaf=4, n_estimators=2..........
[CV 4/5; 32/400] END max_depth=1, min_samples_leaf=4, n_estimators=2;, score=0.790 total time=   0.0s
[CV 5/5; 32/400] START max_depth=1, min_samples_leaf=4, n_estimators=2..........
[CV 5/5; 32/400] END max_depth=1, min_samples_leaf=4, n_estimators=2;, score=0.769 total time=   0.0s
[CV 1/5; 33/400] START max_depth=1, min_samples_leaf=4, n_estimators=3..........
[CV 1/5; 33/400] END max_depth=1, min_samples_leaf=4, n_estimators=3;, score=0.608 total time=   0.0s
[CV 2/5; 33/400] START max_depth=1, min_samples_leaf=4, n_estimators=3..........
[CV 2/5; 33/400] END max_depth=1, min_samples_leaf=4, n_estimators=3;, score=0.622 total time=   0.0s
[CV 3/5; 33/400] START max_depth=1, min_samples_leaf=4, n_estimators=3..........
[CV 3/5; 33/400] END max_depth=1, min_samples_leaf=4, n_estimators=3;, score=0.641 total time=   0.0s
[CV 4/5; 33/400] START max_depth=1, min_samples_leaf=4, n_estimators=3..........
[CV 4/5; 33/400] END max_depth=1, min_samples_leaf=4, n_estimators=3;, score=0.769 total time=   0.0s
[CV 5/5; 33/400] START max_depth=1, min_samples_leaf=4, n_estimators=3..........
[CV 5/5; 33/400] END max_depth=1, min_samples_leaf=4, n_estimators=3;, score=0.763 total time=   0.0s
[CV 1/5; 34/400] START max_depth=1, min_samples_leaf=4, n_estimators=4..........
[CV 1/5; 34/400] END max_depth=1, min_samples_leaf=4, n_estimators=4;, score=0.750 total time=   0.0s
[CV 2/5; 34/400] START max_depth=1, min_samples_leaf=4, n_estimators=4..........
[CV 2/5; 34/400] END max_depth=1, min_samples_leaf=4, n_estimators=4;, score=0.624 total time=   0.0s
[CV 3/5; 34/400] START max_depth=1, min_samples_leaf=4, n_estimators=4..........
[CV 3/5; 34/400] END max_depth=1, min_samples_leaf=4, n_estimators=4;, score=0.608 total time=   0.0s
[CV 4/5; 34/400] START max_depth=1, min_samples_leaf=4, n_estimators=4..........
[CV 4/5; 34/400] END max_depth=1, min_samples_leaf=4, n_estimators=4;, score=0.744 total time=   0.0s
[CV 5/5; 34/400] START max_depth=1, min_samples_leaf=4, n_estimators=4..........
[CV 5/5; 34/400] END max_depth=1, min_samples_leaf=4, n_estimators=4;, score=0.757 total time=   0.0s
[CV 1/5; 35/400] START max_depth=1, min_samples_leaf=4, n_estimators=5..........
[CV 1/5; 35/400] END max_depth=1, min_samples_leaf=4, n_estimators=5;, score=0.731 total time=   0.0s
[CV 2/5; 35/400] START max_depth=1, min_samples_leaf=4, n_estimators=5..........
[CV 2/5; 35/400] END max_depth=1, min_samples_leaf=4, n_estimators=5;, score=0.788 total time=   0.0s
[CV 3/5; 35/400] START max_depth=1, min_samples_leaf=4, n_estimators=5..........
[CV 3/5; 35/400] END max_depth=1, min_samples_leaf=4, n_estimators=5;, score=0.778 total time=   0.0s
[CV 4/5; 35/400] START max_depth=1, min_samples_leaf=4, n_estimators=5..........
[CV 4/5; 35/400] END max_depth=1, min_samples_leaf=4, n_estimators=5;, score=0.788 total time=   0.0s
[CV 5/5; 35/400] START max_depth=1, min_samples_leaf=4, n_estimators=5..........
[CV 5/5; 35/400] END max_depth=1, min_samples_leaf=4, n_estimators=5;, score=0.722 total time=   0.0s
[CV 1/5; 36/400] START max_depth=1, min_samples_leaf=4, n_estimators=6..........
[CV 1/5; 36/400] END max_depth=1, min_samples_leaf=4, n_estimators=6;, score=0.791 total time=   0.0s
[CV 2/5; 36/400] START max_depth=1, min_samples_leaf=4, n_estimators=6..........
[CV 2/5; 36/400] END max_depth=1, min_samples_leaf=4, n_estimators=6;, score=0.788 total time=   0.0s
[CV 3/5; 36/400] START max_depth=1, min_samples_leaf=4, n_estimators=6..........
[CV 3/5; 36/400] END max_depth=1, min_samples_leaf=4, n_estimators=6;, score=0.603 total time=   0.0s
[CV 4/5; 36/400] START max_depth=1, min_samples_leaf=4, n_estimators=6..........
[CV 4/5; 36/400] END max_depth=1, min_samples_leaf=4, n_estimators=6;, score=0.745 total time=   0.0s
[CV 5/5; 36/400] START max_depth=1, min_samples_leaf=4, n_estimators=6..........
[CV 5/5; 36/400] END max_depth=1, min_samples_leaf=4, n_estimators=6;, score=0.769 total time=   0.0s
[CV 1/5; 37/400] START max_depth=1, min_samples_leaf=4, n_estimators=7..........
[CV 1/5; 37/400] END max_depth=1, min_samples_leaf=4, n_estimators=7;, score=0.780 total time=   0.0s
[CV 2/5; 37/400] START max_depth=1, min_samples_leaf=4, n_estimators=7..........
[CV 2/5; 37/400] END max_depth=1, min_samples_leaf=4, n_estimators=7;, score=0.774 total time=   0.0s
[CV 3/5; 37/400] START max_depth=1, min_samples_leaf=4, n_estimators=7..........
[CV 3/5; 37/400] END max_depth=1, min_samples_leaf=4, n_estimators=7;, score=0.780 total time=   0.0s
[CV 4/5; 37/400] START max_depth=1, min_samples_leaf=4, n_estimators=7..........
[CV 4/5; 37/400] END max_depth=1, min_samples_leaf=4, n_estimators=7;, score=0.647 total time=   0.0s
[CV 5/5; 37/400] START max_depth=1, min_samples_leaf=4, n_estimators=7..........
[CV 5/5; 37/400] END max_depth=1, min_samples_leaf=4, n_estimators=7;, score=0.777 total time=   0.0s
[CV 1/5; 38/400] START max_depth=1, min_samples_leaf=4, n_estimators=8..........
[CV 1/5; 38/400] END max_depth=1, min_samples_leaf=4, n_estimators=8;, score=0.794 total time=   0.0s
[CV 2/5; 38/400] START max_depth=1, min_samples_leaf=4, n_estimators=8..........
[CV 2/5; 38/400] END max_depth=1, min_samples_leaf=4, n_estimators=8;, score=0.720 total time=   0.0s
[CV 3/5; 38/400] START max_depth=1, min_samples_leaf=4, n_estimators=8..........
[CV 3/5; 38/400] END max_depth=1, min_samples_leaf=4, n_estimators=8;, score=0.780 total time=   0.0s
[CV 4/5; 38/400] START max_depth=1, min_samples_leaf=4, n_estimators=8..........
[CV 4/5; 38/400] END max_depth=1, min_samples_leaf=4, n_estimators=8;, score=0.769 total time=   0.0s
[CV 5/5; 38/400] START max_depth=1, min_samples_leaf=4, n_estimators=8..........
[CV 5/5; 38/400] END max_depth=1, min_samples_leaf=4, n_estimators=8;, score=0.716 total time=   0.0s
[CV 1/5; 39/400] START max_depth=1, min_samples_leaf=4, n_estimators=9..........
[CV 1/5; 39/400] END max_depth=1, min_samples_leaf=4, n_estimators=9;, score=0.785 total time=   0.0s
[CV 2/5; 39/400] START max_depth=1, min_samples_leaf=4, n_estimators=9..........
[CV 2/5; 39/400] END max_depth=1, min_samples_leaf=4, n_estimators=9;, score=0.788 total time=   0.0s
[CV 3/5; 39/400] START max_depth=1, min_samples_leaf=4, n_estimators=9..........
[CV 3/5; 39/400] END max_depth=1, min_samples_leaf=4, n_estimators=9;, score=0.780 total time=   0.0s
[CV 4/5; 39/400] START max_depth=1, min_samples_leaf=4, n_estimators=9..........
[CV 4/5; 39/400] END max_depth=1, min_samples_leaf=4, n_estimators=9;, score=0.742 total time=   0.0s
[CV 5/5; 39/400] START max_depth=1, min_samples_leaf=4, n_estimators=9..........
[CV 5/5; 39/400] END max_depth=1, min_samples_leaf=4, n_estimators=9;, score=0.764 total time=   0.0s
[CV 1/5; 40/400] START max_depth=1, min_samples_leaf=4, n_estimators=10.........
[CV 1/5; 40/400] END max_depth=1, min_samples_leaf=4, n_estimators=10;, score=0.597 total time=   0.0s
[CV 2/5; 40/400] START max_depth=1, min_samples_leaf=4, n_estimators=10.........
[CV 2/5; 40/400] END max_depth=1, min_samples_leaf=4, n_estimators=10;, score=0.760 total time=   0.0s
[CV 3/5; 40/400] START max_depth=1, min_samples_leaf=4, n_estimators=10.........
[CV 3/5; 40/400] END max_depth=1, min_samples_leaf=4, n_estimators=10;, score=0.771 total time=   0.0s
[CV 4/5; 40/400] START max_depth=1, min_samples_leaf=4, n_estimators=10.........
[CV 4/5; 40/400] END max_depth=1, min_samples_leaf=4, n_estimators=10;, score=0.788 total time=   0.0s
[CV 5/5; 40/400] START max_depth=1, min_samples_leaf=4, n_estimators=10.........
[CV 5/5; 40/400] END max_depth=1, min_samples_leaf=4, n_estimators=10;, score=0.777 total time=   0.0s
[CV 1/5; 41/400] START max_depth=2, min_samples_leaf=1, n_estimators=1..........
[CV 1/5; 41/400] END max_depth=2, min_samples_leaf=1, n_estimators=1;, score=0.641 total time=   0.0s
[CV 2/5; 41/400] START max_depth=2, min_samples_leaf=1, n_estimators=1..........
[CV 2/5; 41/400] END max_depth=2, min_samples_leaf=1, n_estimators=1;, score=0.794 total time=   0.0s
[CV 3/5; 41/400] START max_depth=2, min_samples_leaf=1, n_estimators=1..........
[CV 3/5; 41/400] END max_depth=2, min_samples_leaf=1, n_estimators=1;, score=0.780 total time=   0.0s
[CV 4/5; 41/400] START max_depth=2, min_samples_leaf=1, n_estimators=1..........
[CV 4/5; 41/400] END max_depth=2, min_samples_leaf=1, n_estimators=1;, score=0.790 total time=   0.0s
[CV 5/5; 41/400] START max_depth=2, min_samples_leaf=1, n_estimators=1..........
[CV 5/5; 41/400] END max_depth=2, min_samples_leaf=1, n_estimators=1;, score=0.769 total time=   0.0s
[CV 1/5; 42/400] START max_depth=2, min_samples_leaf=1, n_estimators=2..........
[CV 1/5; 42/400] END max_depth=2, min_samples_leaf=1, n_estimators=2;, score=0.763 total time=   0.0s
[CV 2/5; 42/400] START max_depth=2, min_samples_leaf=1, n_estimators=2..........
[CV 2/5; 42/400] END max_depth=2, min_samples_leaf=1, n_estimators=2;, score=0.741 total time=   0.0s
[CV 3/5; 42/400] START max_depth=2, min_samples_leaf=1, n_estimators=2..........
[CV 3/5; 42/400] END max_depth=2, min_samples_leaf=1, n_estimators=2;, score=0.773 total time=   0.0s
[CV 4/5; 42/400] START max_depth=2, min_samples_leaf=1, n_estimators=2..........
[CV 4/5; 42/400] END max_depth=2, min_samples_leaf=1, n_estimators=2;, score=0.786 total time=   0.0s
[CV 5/5; 42/400] START max_depth=2, min_samples_leaf=1, n_estimators=2..........
[CV 5/5; 42/400] END max_depth=2, min_samples_leaf=1, n_estimators=2;, score=0.709 total time=   0.0s
[CV 1/5; 43/400] START max_depth=2, min_samples_leaf=1, n_estimators=3..........
[CV 1/5; 43/400] END max_depth=2, min_samples_leaf=1, n_estimators=3;, score=0.796 total time=   0.0s
[CV 2/5; 43/400] START max_depth=2, min_samples_leaf=1, n_estimators=3..........
[CV 2/5; 43/400] END max_depth=2, min_samples_leaf=1, n_estimators=3;, score=0.769 total time=   0.0s
[CV 3/5; 43/400] START max_depth=2, min_samples_leaf=1, n_estimators=3..........
[CV 3/5; 43/400] END max_depth=2, min_samples_leaf=1, n_estimators=3;, score=0.760 total time=   0.0s
[CV 4/5; 43/400] START max_depth=2, min_samples_leaf=1, n_estimators=3..........
[CV 4/5; 43/400] END max_depth=2, min_samples_leaf=1, n_estimators=3;, score=0.790 total time=   0.0s
[CV 5/5; 43/400] START max_depth=2, min_samples_leaf=1, n_estimators=3..........
[CV 5/5; 43/400] END max_depth=2, min_samples_leaf=1, n_estimators=3;, score=0.771 total time=   0.0s
[CV 1/5; 44/400] START max_depth=2, min_samples_leaf=1, n_estimators=4..........
[CV 1/5; 44/400] END max_depth=2, min_samples_leaf=1, n_estimators=4;, score=0.758 total time=   0.0s
[CV 2/5; 44/400] START max_depth=2, min_samples_leaf=1, n_estimators=4..........
[CV 2/5; 44/400] END max_depth=2, min_samples_leaf=1, n_estimators=4;, score=0.788 total time=   0.0s
[CV 3/5; 44/400] START max_depth=2, min_samples_leaf=1, n_estimators=4..........
[CV 3/5; 44/400] END max_depth=2, min_samples_leaf=1, n_estimators=4;, score=0.780 total time=   0.0s
[CV 4/5; 44/400] START max_depth=2, min_samples_leaf=1, n_estimators=4..........
[CV 4/5; 44/400] END max_depth=2, min_samples_leaf=1, n_estimators=4;, score=0.744 total time=   0.0s
[CV 5/5; 44/400] START max_depth=2, min_samples_leaf=1, n_estimators=4..........
[CV 5/5; 44/400] END max_depth=2, min_samples_leaf=1, n_estimators=4;, score=0.694 total time=   0.0s
[CV 1/5; 45/400] START max_depth=2, min_samples_leaf=1, n_estimators=5..........
[CV 1/5; 45/400] END max_depth=2, min_samples_leaf=1, n_estimators=5;, score=0.794 total time=   0.0s
[CV 2/5; 45/400] START max_depth=2, min_samples_leaf=1, n_estimators=5..........
[CV 2/5; 45/400] END max_depth=2, min_samples_leaf=1, n_estimators=5;, score=0.766 total time=   0.0s
[CV 3/5; 45/400] START max_depth=2, min_samples_leaf=1, n_estimators=5..........
[CV 3/5; 45/400] END max_depth=2, min_samples_leaf=1, n_estimators=5;, score=0.780 total time=   0.0s
[CV 4/5; 45/400] START max_depth=2, min_samples_leaf=1, n_estimators=5..........
[CV 4/5; 45/400] END max_depth=2, min_samples_leaf=1, n_estimators=5;, score=0.773 total time=   0.0s
[CV 5/5; 45/400] START max_depth=2, min_samples_leaf=1, n_estimators=5..........
[CV 5/5; 45/400] END max_depth=2, min_samples_leaf=1, n_estimators=5;, score=0.768 total time=   0.0s
[CV 1/5; 46/400] START max_depth=2, min_samples_leaf=1, n_estimators=6..........
[CV 1/5; 46/400] END max_depth=2, min_samples_leaf=1, n_estimators=6;, score=0.745 total time=   0.0s
[CV 2/5; 46/400] START max_depth=2, min_samples_leaf=1, n_estimators=6..........
[CV 2/5; 46/400] END max_depth=2, min_samples_leaf=1, n_estimators=6;, score=0.790 total time=   0.0s
[CV 3/5; 46/400] START max_depth=2, min_samples_leaf=1, n_estimators=6..........
[CV 3/5; 46/400] END max_depth=2, min_samples_leaf=1, n_estimators=6;, score=0.764 total time=   0.0s
[CV 4/5; 46/400] START max_depth=2, min_samples_leaf=1, n_estimators=6..........
[CV 4/5; 46/400] END max_depth=2, min_samples_leaf=1, n_estimators=6;, score=0.790 total time=   0.0s
[CV 5/5; 46/400] START max_depth=2, min_samples_leaf=1, n_estimators=6..........
[CV 5/5; 46/400] END max_depth=2, min_samples_leaf=1, n_estimators=6;, score=0.777 total time=   0.0s
[CV 1/5; 47/400] START max_depth=2, min_samples_leaf=1, n_estimators=7..........
[CV 1/5; 47/400] END max_depth=2, min_samples_leaf=1, n_estimators=7;, score=0.763 total time=   0.0s
[CV 2/5; 47/400] START max_depth=2, min_samples_leaf=1, n_estimators=7..........
[CV 2/5; 47/400] END max_depth=2, min_samples_leaf=1, n_estimators=7;, score=0.783 total time=   0.0s
[CV 3/5; 47/400] START max_depth=2, min_samples_leaf=1, n_estimators=7..........
[CV 3/5; 47/400] END max_depth=2, min_samples_leaf=1, n_estimators=7;, score=0.780 total time=   0.0s
[CV 4/5; 47/400] START max_depth=2, min_samples_leaf=1, n_estimators=7..........
[CV 4/5; 47/400] END max_depth=2, min_samples_leaf=1, n_estimators=7;, score=0.789 total time=   0.0s
[CV 5/5; 47/400] START max_depth=2, min_samples_leaf=1, n_estimators=7..........
[CV 5/5; 47/400] END max_depth=2, min_samples_leaf=1, n_estimators=7;, score=0.772 total time=   0.0s
[CV 1/5; 48/400] START max_depth=2, min_samples_leaf=1, n_estimators=8..........
[CV 1/5; 48/400] END max_depth=2, min_samples_leaf=1, n_estimators=8;, score=0.769 total time=   0.0s
[CV 2/5; 48/400] START max_depth=2, min_samples_leaf=1, n_estimators=8..........
[CV 2/5; 48/400] END max_depth=2, min_samples_leaf=1, n_estimators=8;, score=0.773 total time=   0.0s
[CV 3/5; 48/400] START max_depth=2, min_samples_leaf=1, n_estimators=8..........
[CV 3/5; 48/400] END max_depth=2, min_samples_leaf=1, n_estimators=8;, score=0.780 total time=   0.0s
[CV 4/5; 48/400] START max_depth=2, min_samples_leaf=1, n_estimators=8..........
[CV 4/5; 48/400] END max_depth=2, min_samples_leaf=1, n_estimators=8;, score=0.795 total time=   0.0s
[CV 5/5; 48/400] START max_depth=2, min_samples_leaf=1, n_estimators=8..........
[CV 5/5; 48/400] END max_depth=2, min_samples_leaf=1, n_estimators=8;, score=0.758 total time=   0.0s
[CV 1/5; 49/400] START max_depth=2, min_samples_leaf=1, n_estimators=9..........
[CV 1/5; 49/400] END max_depth=2, min_samples_leaf=1, n_estimators=9;, score=0.794 total time=   0.0s
[CV 2/5; 49/400] START max_depth=2, min_samples_leaf=1, n_estimators=9..........
[CV 2/5; 49/400] END max_depth=2, min_samples_leaf=1, n_estimators=9;, score=0.782 total time=   0.0s
[CV 3/5; 49/400] START max_depth=2, min_samples_leaf=1, n_estimators=9..........
[CV 3/5; 49/400] END max_depth=2, min_samples_leaf=1, n_estimators=9;, score=0.777 total time=   0.0s
[CV 4/5; 49/400] START max_depth=2, min_samples_leaf=1, n_estimators=9..........
[CV 4/5; 49/400] END max_depth=2, min_samples_leaf=1, n_estimators=9;, score=0.790 total time=   0.0s
[CV 5/5; 49/400] START max_depth=2, min_samples_leaf=1, n_estimators=9..........
[CV 5/5; 49/400] END max_depth=2, min_samples_leaf=1, n_estimators=9;, score=0.773 total time=   0.0s
[CV 1/5; 50/400] START max_depth=2, min_samples_leaf=1, n_estimators=10.........
[CV 1/5; 50/400] END max_depth=2, min_samples_leaf=1, n_estimators=10;, score=0.789 total time=   0.0s
[CV 2/5; 50/400] START max_depth=2, min_samples_leaf=1, n_estimators=10.........
[CV 2/5; 50/400] END max_depth=2, min_samples_leaf=1, n_estimators=10;, score=0.788 total time=   0.0s
[CV 3/5; 50/400] START max_depth=2, min_samples_leaf=1, n_estimators=10.........
[CV 3/5; 50/400] END max_depth=2, min_samples_leaf=1, n_estimators=10;, score=0.780 total time=   0.0s
[CV 4/5; 50/400] START max_depth=2, min_samples_leaf=1, n_estimators=10.........
[CV 4/5; 50/400] END max_depth=2, min_samples_leaf=1, n_estimators=10;, score=0.789 total time=   0.0s
[CV 5/5; 50/400] START max_depth=2, min_samples_leaf=1, n_estimators=10.........
[CV 5/5; 50/400] END max_depth=2, min_samples_leaf=1, n_estimators=10;, score=0.778 total time=   0.0s
[CV 1/5; 51/400] START max_depth=2, min_samples_leaf=2, n_estimators=1..........
[CV 1/5; 51/400] END max_depth=2, min_samples_leaf=2, n_estimators=1;, score=0.774 total time=   0.0s
[CV 2/5; 51/400] START max_depth=2, min_samples_leaf=2, n_estimators=1..........
[CV 2/5; 51/400] END max_depth=2, min_samples_leaf=2, n_estimators=1;, score=0.597 total time=   0.0s
[CV 3/5; 51/400] START max_depth=2, min_samples_leaf=2, n_estimators=1..........
[CV 3/5; 51/400] END max_depth=2, min_samples_leaf=2, n_estimators=1;, score=0.604 total time=   0.0s
[CV 4/5; 51/400] START max_depth=2, min_samples_leaf=2, n_estimators=1..........
[CV 4/5; 51/400] END max_depth=2, min_samples_leaf=2, n_estimators=1;, score=0.788 total time=   0.0s
[CV 5/5; 51/400] START max_depth=2, min_samples_leaf=2, n_estimators=1..........
[CV 5/5; 51/400] END max_depth=2, min_samples_leaf=2, n_estimators=1;, score=0.613 total time=   0.0s
[CV 1/5; 52/400] START max_depth=2, min_samples_leaf=2, n_estimators=2..........
[CV 1/5; 52/400] END max_depth=2, min_samples_leaf=2, n_estimators=2;, score=0.625 total time=   0.0s
[CV 2/5; 52/400] START max_depth=2, min_samples_leaf=2, n_estimators=2..........
[CV 2/5; 52/400] END max_depth=2, min_samples_leaf=2, n_estimators=2;, score=0.752 total time=   0.0s
[CV 3/5; 52/400] START max_depth=2, min_samples_leaf=2, n_estimators=2..........
[CV 3/5; 52/400] END max_depth=2, min_samples_leaf=2, n_estimators=2;, score=0.776 total time=   0.0s
[CV 4/5; 52/400] START max_depth=2, min_samples_leaf=2, n_estimators=2..........
[CV 4/5; 52/400] END max_depth=2, min_samples_leaf=2, n_estimators=2;, score=0.791 total time=   0.0s
[CV 5/5; 52/400] START max_depth=2, min_samples_leaf=2, n_estimators=2..........
[CV 5/5; 52/400] END max_depth=2, min_samples_leaf=2, n_estimators=2;, score=0.773 total time=   0.0s
[CV 1/5; 53/400] START max_depth=2, min_samples_leaf=2, n_estimators=3..........
[CV 1/5; 53/400] END max_depth=2, min_samples_leaf=2, n_estimators=3;, score=0.785 total time=   0.0s
[CV 2/5; 53/400] START max_depth=2, min_samples_leaf=2, n_estimators=3..........
[CV 2/5; 53/400] END max_depth=2, min_samples_leaf=2, n_estimators=3;, score=0.740 total time=   0.0s
[CV 3/5; 53/400] START max_depth=2, min_samples_leaf=2, n_estimators=3..........
[CV 3/5; 53/400] END max_depth=2, min_samples_leaf=2, n_estimators=3;, score=0.766 total time=   0.0s
[CV 4/5; 53/400] START max_depth=2, min_samples_leaf=2, n_estimators=3..........
[CV 4/5; 53/400] END max_depth=2, min_samples_leaf=2, n_estimators=3;, score=0.788 total time=   0.0s
[CV 5/5; 53/400] START max_depth=2, min_samples_leaf=2, n_estimators=3..........
[CV 5/5; 53/400] END max_depth=2, min_samples_leaf=2, n_estimators=3;, score=0.779 total time=   0.0s
[CV 1/5; 54/400] START max_depth=2, min_samples_leaf=2, n_estimators=4..........
[CV 1/5; 54/400] END max_depth=2, min_samples_leaf=2, n_estimators=4;, score=0.788 total time=   0.0s
[CV 2/5; 54/400] START max_depth=2, min_samples_leaf=2, n_estimators=4..........
[CV 2/5; 54/400] END max_depth=2, min_samples_leaf=2, n_estimators=4;, score=0.792 total time=   0.0s
[CV 3/5; 54/400] START max_depth=2, min_samples_leaf=2, n_estimators=4..........
[CV 3/5; 54/400] END max_depth=2, min_samples_leaf=2, n_estimators=4;, score=0.760 total time=   0.0s
[CV 4/5; 54/400] START max_depth=2, min_samples_leaf=2, n_estimators=4..........
[CV 4/5; 54/400] END max_depth=2, min_samples_leaf=2, n_estimators=4;, score=0.789 total time=   0.0s
[CV 5/5; 54/400] START max_depth=2, min_samples_leaf=2, n_estimators=4..........
[CV 5/5; 54/400] END max_depth=2, min_samples_leaf=2, n_estimators=4;, score=0.770 total time=   0.0s
[CV 1/5; 55/400] START max_depth=2, min_samples_leaf=2, n_estimators=5..........
[CV 1/5; 55/400] END max_depth=2, min_samples_leaf=2, n_estimators=5;, score=0.787 total time=   0.0s
[CV 2/5; 55/400] START max_depth=2, min_samples_leaf=2, n_estimators=5..........
[CV 2/5; 55/400] END max_depth=2, min_samples_leaf=2, n_estimators=5;, score=0.788 total time=   0.0s
[CV 3/5; 55/400] START max_depth=2, min_samples_leaf=2, n_estimators=5..........
[CV 3/5; 55/400] END max_depth=2, min_samples_leaf=2, n_estimators=5;, score=0.780 total time=   0.0s
[CV 4/5; 55/400] START max_depth=2, min_samples_leaf=2, n_estimators=5..........
[CV 4/5; 55/400] END max_depth=2, min_samples_leaf=2, n_estimators=5;, score=0.790 total time=   0.0s
[CV 5/5; 55/400] START max_depth=2, min_samples_leaf=2, n_estimators=5..........
[CV 5/5; 55/400] END max_depth=2, min_samples_leaf=2, n_estimators=5;, score=0.760 total time=   0.0s
[CV 1/5; 56/400] START max_depth=2, min_samples_leaf=2, n_estimators=6..........
[CV 1/5; 56/400] END max_depth=2, min_samples_leaf=2, n_estimators=6;, score=0.714 total time=   0.0s
[CV 2/5; 56/400] START max_depth=2, min_samples_leaf=2, n_estimators=6..........
[CV 2/5; 56/400] END max_depth=2, min_samples_leaf=2, n_estimators=6;, score=0.788 total time=   0.0s
[CV 3/5; 56/400] START max_depth=2, min_samples_leaf=2, n_estimators=6..........
[CV 3/5; 56/400] END max_depth=2, min_samples_leaf=2, n_estimators=6;, score=0.780 total time=   0.0s
[CV 4/5; 56/400] START max_depth=2, min_samples_leaf=2, n_estimators=6..........
[CV 4/5; 56/400] END max_depth=2, min_samples_leaf=2, n_estimators=6;, score=0.790 total time=   0.0s
[CV 5/5; 56/400] START max_depth=2, min_samples_leaf=2, n_estimators=6..........
[CV 5/5; 56/400] END max_depth=2, min_samples_leaf=2, n_estimators=6;, score=0.769 total time=   0.0s
[CV 1/5; 57/400] START max_depth=2, min_samples_leaf=2, n_estimators=7..........
[CV 1/5; 57/400] END max_depth=2, min_samples_leaf=2, n_estimators=7;, score=0.796 total time=   0.0s
[CV 2/5; 57/400] START max_depth=2, min_samples_leaf=2, n_estimators=7..........
[CV 2/5; 57/400] END max_depth=2, min_samples_leaf=2, n_estimators=7;, score=0.791 total time=   0.0s
[CV 3/5; 57/400] START max_depth=2, min_samples_leaf=2, n_estimators=7..........
[CV 3/5; 57/400] END max_depth=2, min_samples_leaf=2, n_estimators=7;, score=0.780 total time=   0.0s
[CV 4/5; 57/400] START max_depth=2, min_samples_leaf=2, n_estimators=7..........
[CV 4/5; 57/400] END max_depth=2, min_samples_leaf=2, n_estimators=7;, score=0.791 total time=   0.0s
[CV 5/5; 57/400] START max_depth=2, min_samples_leaf=2, n_estimators=7..........
[CV 5/5; 57/400] END max_depth=2, min_samples_leaf=2, n_estimators=7;, score=0.772 total time=   0.0s
[CV 1/5; 58/400] START max_depth=2, min_samples_leaf=2, n_estimators=8..........
[CV 1/5; 58/400] END max_depth=2, min_samples_leaf=2, n_estimators=8;, score=0.789 total time=   0.0s
[CV 2/5; 58/400] START max_depth=2, min_samples_leaf=2, n_estimators=8..........
[CV 2/5; 58/400] END max_depth=2, min_samples_leaf=2, n_estimators=8;, score=0.788 total time=   0.0s
[CV 3/5; 58/400] START max_depth=2, min_samples_leaf=2, n_estimators=8..........
[CV 3/5; 58/400] END max_depth=2, min_samples_leaf=2, n_estimators=8;, score=0.774 total time=   0.0s
[CV 4/5; 58/400] START max_depth=2, min_samples_leaf=2, n_estimators=8..........
[CV 4/5; 58/400] END max_depth=2, min_samples_leaf=2, n_estimators=8;, score=0.792 total time=   0.0s
[CV 5/5; 58/400] START max_depth=2, min_samples_leaf=2, n_estimators=8..........
[CV 5/5; 58/400] END max_depth=2, min_samples_leaf=2, n_estimators=8;, score=0.772 total time=   0.0s
[CV 1/5; 59/400] START max_depth=2, min_samples_leaf=2, n_estimators=9..........
[CV 1/5; 59/400] END max_depth=2, min_samples_leaf=2, n_estimators=9;, score=0.794 total time=   0.0s
[CV 2/5; 59/400] START max_depth=2, min_samples_leaf=2, n_estimators=9..........
[CV 2/5; 59/400] END max_depth=2, min_samples_leaf=2, n_estimators=9;, score=0.788 total time=   0.0s
[CV 3/5; 59/400] START max_depth=2, min_samples_leaf=2, n_estimators=9..........
[CV 3/5; 59/400] END max_depth=2, min_samples_leaf=2, n_estimators=9;, score=0.776 total time=   0.0s
[CV 4/5; 59/400] START max_depth=2, min_samples_leaf=2, n_estimators=9..........
[CV 4/5; 59/400] END max_depth=2, min_samples_leaf=2, n_estimators=9;, score=0.768 total time=   0.0s
[CV 5/5; 59/400] START max_depth=2, min_samples_leaf=2, n_estimators=9..........
[CV 5/5; 59/400] END max_depth=2, min_samples_leaf=2, n_estimators=9;, score=0.777 total time=   0.0s
[CV 1/5; 60/400] START max_depth=2, min_samples_leaf=2, n_estimators=10.........
[CV 1/5; 60/400] END max_depth=2, min_samples_leaf=2, n_estimators=10;, score=0.796 total time=   0.0s
[CV 2/5; 60/400] START max_depth=2, min_samples_leaf=2, n_estimators=10.........
[CV 2/5; 60/400] END max_depth=2, min_samples_leaf=2, n_estimators=10;, score=0.788 total time=   0.1s
[CV 3/5; 60/400] START max_depth=2, min_samples_leaf=2, n_estimators=10.........
[CV 3/5; 60/400] END max_depth=2, min_samples_leaf=2, n_estimators=10;, score=0.782 total time=   0.0s
[CV 4/5; 60/400] START max_depth=2, min_samples_leaf=2, n_estimators=10.........
[CV 4/5; 60/400] END max_depth=2, min_samples_leaf=2, n_estimators=10;, score=0.793 total time=   0.0s
[CV 5/5; 60/400] START max_depth=2, min_samples_leaf=2, n_estimators=10.........
[CV 5/5; 60/400] END max_depth=2, min_samples_leaf=2, n_estimators=10;, score=0.774 total time=   0.0s
[CV 1/5; 61/400] START max_depth=2, min_samples_leaf=3, n_estimators=1..........
[CV 1/5; 61/400] END max_depth=2, min_samples_leaf=3, n_estimators=1;, score=0.787 total time=   0.0s
[CV 2/5; 61/400] START max_depth=2, min_samples_leaf=3, n_estimators=1..........
[CV 2/5; 61/400] END max_depth=2, min_samples_leaf=3, n_estimators=1;, score=0.630 total time=   0.0s
[CV 3/5; 61/400] START max_depth=2, min_samples_leaf=3, n_estimators=1..........
[CV 3/5; 61/400] END max_depth=2, min_samples_leaf=3, n_estimators=1;, score=0.613 total time=   0.0s
[CV 4/5; 61/400] START max_depth=2, min_samples_leaf=3, n_estimators=1..........
[CV 4/5; 61/400] END max_depth=2, min_samples_leaf=3, n_estimators=1;, score=0.790 total time=   0.0s
[CV 5/5; 61/400] START max_depth=2, min_samples_leaf=3, n_estimators=1..........
[CV 5/5; 61/400] END max_depth=2, min_samples_leaf=3, n_estimators=1;, score=0.589 total time=   0.0s
[CV 1/5; 62/400] START max_depth=2, min_samples_leaf=3, n_estimators=2..........
[CV 1/5; 62/400] END max_depth=2, min_samples_leaf=3, n_estimators=2;, score=0.597 total time=   0.0s
[CV 2/5; 62/400] START max_depth=2, min_samples_leaf=3, n_estimators=2..........
[CV 2/5; 62/400] END max_depth=2, min_samples_leaf=3, n_estimators=2;, score=0.672 total time=   0.0s
[CV 3/5; 62/400] START max_depth=2, min_samples_leaf=3, n_estimators=2..........
[CV 3/5; 62/400] END max_depth=2, min_samples_leaf=3, n_estimators=2;, score=0.771 total time=   0.0s
[CV 4/5; 62/400] START max_depth=2, min_samples_leaf=3, n_estimators=2..........
[CV 4/5; 62/400] END max_depth=2, min_samples_leaf=3, n_estimators=2;, score=0.790 total time=   0.0s
[CV 5/5; 62/400] START max_depth=2, min_samples_leaf=3, n_estimators=2..........
[CV 5/5; 62/400] END max_depth=2, min_samples_leaf=3, n_estimators=2;, score=0.771 total time=   0.0s
[CV 1/5; 63/400] START max_depth=2, min_samples_leaf=3, n_estimators=3..........
[CV 1/5; 63/400] END max_depth=2, min_samples_leaf=3, n_estimators=3;, score=0.756 total time=   0.0s
[CV 2/5; 63/400] START max_depth=2, min_samples_leaf=3, n_estimators=3..........
[CV 2/5; 63/400] END max_depth=2, min_samples_leaf=3, n_estimators=3;, score=0.786 total time=   0.0s
[CV 3/5; 63/400] START max_depth=2, min_samples_leaf=3, n_estimators=3..........
[CV 3/5; 63/400] END max_depth=2, min_samples_leaf=3, n_estimators=3;, score=0.780 total time=   0.0s
[CV 4/5; 63/400] START max_depth=2, min_samples_leaf=3, n_estimators=3..........
[CV 4/5; 63/400] END max_depth=2, min_samples_leaf=3, n_estimators=3;, score=0.761 total time=   0.0s
[CV 5/5; 63/400] START max_depth=2, min_samples_leaf=3, n_estimators=3..........
[CV 5/5; 63/400] END max_depth=2, min_samples_leaf=3, n_estimators=3;, score=0.770 total time=   0.0s
[CV 1/5; 64/400] START max_depth=2, min_samples_leaf=3, n_estimators=4..........
[CV 1/5; 64/400] END max_depth=2, min_samples_leaf=3, n_estimators=4;, score=0.796 total time=   0.0s
[CV 2/5; 64/400] START max_depth=2, min_samples_leaf=3, n_estimators=4..........
[CV 2/5; 64/400] END max_depth=2, min_samples_leaf=3, n_estimators=4;, score=0.793 total time=   0.0s
[CV 3/5; 64/400] START max_depth=2, min_samples_leaf=3, n_estimators=4..........
[CV 3/5; 64/400] END max_depth=2, min_samples_leaf=3, n_estimators=4;, score=0.781 total time=   0.0s
[CV 4/5; 64/400] START max_depth=2, min_samples_leaf=3, n_estimators=4..........
[CV 4/5; 64/400] END max_depth=2, min_samples_leaf=3, n_estimators=4;, score=0.790 total time=   0.0s
[CV 5/5; 64/400] START max_depth=2, min_samples_leaf=3, n_estimators=4..........
[CV 5/5; 64/400] END max_depth=2, min_samples_leaf=3, n_estimators=4;, score=0.776 total time=   0.0s
[CV 1/5; 65/400] START max_depth=2, min_samples_leaf=3, n_estimators=5..........
[CV 1/5; 65/400] END max_depth=2, min_samples_leaf=3, n_estimators=5;, score=0.753 total time=   0.0s
[CV 2/5; 65/400] START max_depth=2, min_samples_leaf=3, n_estimators=5..........
[CV 2/5; 65/400] END max_depth=2, min_samples_leaf=3, n_estimators=5;, score=0.794 total time=   0.0s
[CV 3/5; 65/400] START max_depth=2, min_samples_leaf=3, n_estimators=5..........
[CV 3/5; 65/400] END max_depth=2, min_samples_leaf=3, n_estimators=5;, score=0.770 total time=   0.0s
[CV 4/5; 65/400] START max_depth=2, min_samples_leaf=3, n_estimators=5..........
[CV 4/5; 65/400] END max_depth=2, min_samples_leaf=3, n_estimators=5;, score=0.790 total time=   0.0s
[CV 5/5; 65/400] START max_depth=2, min_samples_leaf=3, n_estimators=5..........
[CV 5/5; 65/400] END max_depth=2, min_samples_leaf=3, n_estimators=5;, score=0.766 total time=   0.0s
[CV 1/5; 66/400] START max_depth=2, min_samples_leaf=3, n_estimators=6..........
[CV 1/5; 66/400] END max_depth=2, min_samples_leaf=3, n_estimators=6;, score=0.799 total time=   0.0s
[CV 2/5; 66/400] START max_depth=2, min_samples_leaf=3, n_estimators=6..........
[CV 2/5; 66/400] END max_depth=2, min_samples_leaf=3, n_estimators=6;, score=0.788 total time=   0.0s
[CV 3/5; 66/400] START max_depth=2, min_samples_leaf=3, n_estimators=6..........
[CV 3/5; 66/400] END max_depth=2, min_samples_leaf=3, n_estimators=6;, score=0.778 total time=   0.0s
[CV 4/5; 66/400] START max_depth=2, min_samples_leaf=3, n_estimators=6..........
[CV 4/5; 66/400] END max_depth=2, min_samples_leaf=3, n_estimators=6;, score=0.785 total time=   0.0s
[CV 5/5; 66/400] START max_depth=2, min_samples_leaf=3, n_estimators=6..........
[CV 5/5; 66/400] END max_depth=2, min_samples_leaf=3, n_estimators=6;, score=0.771 total time=   0.0s
[CV 1/5; 67/400] START max_depth=2, min_samples_leaf=3, n_estimators=7..........
[CV 1/5; 67/400] END max_depth=2, min_samples_leaf=3, n_estimators=7;, score=0.799 total time=   0.0s
[CV 2/5; 67/400] START max_depth=2, min_samples_leaf=3, n_estimators=7..........
[CV 2/5; 67/400] END max_depth=2, min_samples_leaf=3, n_estimators=7;, score=0.785 total time=   0.0s
[CV 3/5; 67/400] START max_depth=2, min_samples_leaf=3, n_estimators=7..........
[CV 3/5; 67/400] END max_depth=2, min_samples_leaf=3, n_estimators=7;, score=0.752 total time=   0.0s
[CV 4/5; 67/400] START max_depth=2, min_samples_leaf=3, n_estimators=7..........
[CV 4/5; 67/400] END max_depth=2, min_samples_leaf=3, n_estimators=7;, score=0.793 total time=   0.0s
[CV 5/5; 67/400] START max_depth=2, min_samples_leaf=3, n_estimators=7..........
[CV 5/5; 67/400] END max_depth=2, min_samples_leaf=3, n_estimators=7;, score=0.734 total time=   0.0s
[CV 1/5; 68/400] START max_depth=2, min_samples_leaf=3, n_estimators=8..........
[CV 1/5; 68/400] END max_depth=2, min_samples_leaf=3, n_estimators=8;, score=0.743 total time=   0.0s
[CV 2/5; 68/400] START max_depth=2, min_samples_leaf=3, n_estimators=8..........
[CV 2/5; 68/400] END max_depth=2, min_samples_leaf=3, n_estimators=8;, score=0.768 total time=   0.0s
[CV 3/5; 68/400] START max_depth=2, min_samples_leaf=3, n_estimators=8..........
[CV 3/5; 68/400] END max_depth=2, min_samples_leaf=3, n_estimators=8;, score=0.752 total time=   0.0s
[CV 4/5; 68/400] START max_depth=2, min_samples_leaf=3, n_estimators=8..........
[CV 4/5; 68/400] END max_depth=2, min_samples_leaf=3, n_estimators=8;, score=0.788 total time=   0.0s
[CV 5/5; 68/400] START max_depth=2, min_samples_leaf=3, n_estimators=8..........
[CV 5/5; 68/400] END max_depth=2, min_samples_leaf=3, n_estimators=8;, score=0.770 total time=   0.0s
[CV 1/5; 69/400] START max_depth=2, min_samples_leaf=3, n_estimators=9..........
[CV 1/5; 69/400] END max_depth=2, min_samples_leaf=3, n_estimators=9;, score=0.791 total time=   0.1s
[CV 2/5; 69/400] START max_depth=2, min_samples_leaf=3, n_estimators=9..........
[CV 2/5; 69/400] END max_depth=2, min_samples_leaf=3, n_estimators=9;, score=0.794 total time=   0.0s
[CV 3/5; 69/400] START max_depth=2, min_samples_leaf=3, n_estimators=9..........
[CV 3/5; 69/400] END max_depth=2, min_samples_leaf=3, n_estimators=9;, score=0.782 total time=   0.0s
[CV 4/5; 69/400] START max_depth=2, min_samples_leaf=3, n_estimators=9..........
[CV 4/5; 69/400] END max_depth=2, min_samples_leaf=3, n_estimators=9;, score=0.791 total time=   0.0s
[CV 5/5; 69/400] START max_depth=2, min_samples_leaf=3, n_estimators=9..........
[CV 5/5; 69/400] END max_depth=2, min_samples_leaf=3, n_estimators=9;, score=0.777 total time=   0.0s
[CV 1/5; 70/400] START max_depth=2, min_samples_leaf=3, n_estimators=10.........
[CV 1/5; 70/400] END max_depth=2, min_samples_leaf=3, n_estimators=10;, score=0.791 total time=   0.0s
[CV 2/5; 70/400] START max_depth=2, min_samples_leaf=3, n_estimators=10.........
[CV 2/5; 70/400] END max_depth=2, min_samples_leaf=3, n_estimators=10;, score=0.789 total time=   0.0s
[CV 3/5; 70/400] START max_depth=2, min_samples_leaf=3, n_estimators=10.........
[CV 3/5; 70/400] END max_depth=2, min_samples_leaf=3, n_estimators=10;, score=0.767 total time=   0.0s
[CV 4/5; 70/400] START max_depth=2, min_samples_leaf=3, n_estimators=10.........
[CV 4/5; 70/400] END max_depth=2, min_samples_leaf=3, n_estimators=10;, score=0.788 total time=   0.0s
[CV 5/5; 70/400] START max_depth=2, min_samples_leaf=3, n_estimators=10.........
[CV 5/5; 70/400] END max_depth=2, min_samples_leaf=3, n_estimators=10;, score=0.776 total time=   0.0s
[CV 1/5; 71/400] START max_depth=2, min_samples_leaf=4, n_estimators=1..........
[CV 1/5; 71/400] END max_depth=2, min_samples_leaf=4, n_estimators=1;, score=0.788 total time=   0.0s
[CV 2/5; 71/400] START max_depth=2, min_samples_leaf=4, n_estimators=1..........
[CV 2/5; 71/400] END max_depth=2, min_samples_leaf=4, n_estimators=1;, score=0.734 total time=   0.0s
[CV 3/5; 71/400] START max_depth=2, min_samples_leaf=4, n_estimators=1..........
[CV 3/5; 71/400] END max_depth=2, min_samples_leaf=4, n_estimators=1;, score=0.755 total time=   0.0s
[CV 4/5; 71/400] START max_depth=2, min_samples_leaf=4, n_estimators=1..........
[CV 4/5; 71/400] END max_depth=2, min_samples_leaf=4, n_estimators=1;, score=0.756 total time=   0.0s
[CV 5/5; 71/400] START max_depth=2, min_samples_leaf=4, n_estimators=1..........
[CV 5/5; 71/400] END max_depth=2, min_samples_leaf=4, n_estimators=1;, score=0.769 total time=   0.0s
[CV 1/5; 72/400] START max_depth=2, min_samples_leaf=4, n_estimators=2..........
[CV 1/5; 72/400] END max_depth=2, min_samples_leaf=4, n_estimators=2;, score=0.791 total time=   0.0s
[CV 2/5; 72/400] START max_depth=2, min_samples_leaf=4, n_estimators=2..........
[CV 2/5; 72/400] END max_depth=2, min_samples_leaf=4, n_estimators=2;, score=0.789 total time=   0.0s
[CV 3/5; 72/400] START max_depth=2, min_samples_leaf=4, n_estimators=2..........
[CV 3/5; 72/400] END max_depth=2, min_samples_leaf=4, n_estimators=2;, score=0.780 total time=   0.0s
[CV 4/5; 72/400] START max_depth=2, min_samples_leaf=4, n_estimators=2..........
[CV 4/5; 72/400] END max_depth=2, min_samples_leaf=4, n_estimators=2;, score=0.790 total time=   0.0s
[CV 5/5; 72/400] START max_depth=2, min_samples_leaf=4, n_estimators=2..........
[CV 5/5; 72/400] END max_depth=2, min_samples_leaf=4, n_estimators=2;, score=0.602 total time=   0.0s
[CV 1/5; 73/400] START max_depth=2, min_samples_leaf=4, n_estimators=3..........
[CV 1/5; 73/400] END max_depth=2, min_samples_leaf=4, n_estimators=3;, score=0.771 total time=   0.0s
[CV 2/5; 73/400] START max_depth=2, min_samples_leaf=4, n_estimators=3..........
[CV 2/5; 73/400] END max_depth=2, min_samples_leaf=4, n_estimators=3;, score=0.790 total time=   0.0s
[CV 3/5; 73/400] START max_depth=2, min_samples_leaf=4, n_estimators=3..........
[CV 3/5; 73/400] END max_depth=2, min_samples_leaf=4, n_estimators=3;, score=0.783 total time=   0.0s
[CV 4/5; 73/400] START max_depth=2, min_samples_leaf=4, n_estimators=3..........
[CV 4/5; 73/400] END max_depth=2, min_samples_leaf=4, n_estimators=3;, score=0.794 total time=   0.0s
[CV 5/5; 73/400] START max_depth=2, min_samples_leaf=4, n_estimators=3..........
[CV 5/5; 73/400] END max_depth=2, min_samples_leaf=4, n_estimators=3;, score=0.771 total time=   0.0s
[CV 1/5; 74/400] START max_depth=2, min_samples_leaf=4, n_estimators=4..........
[CV 1/5; 74/400] END max_depth=2, min_samples_leaf=4, n_estimators=4;, score=0.791 total time=   0.0s
[CV 2/5; 74/400] START max_depth=2, min_samples_leaf=4, n_estimators=4..........
[CV 2/5; 74/400] END max_depth=2, min_samples_leaf=4, n_estimators=4;, score=0.788 total time=   0.0s
[CV 3/5; 74/400] START max_depth=2, min_samples_leaf=4, n_estimators=4..........
[CV 3/5; 74/400] END max_depth=2, min_samples_leaf=4, n_estimators=4;, score=0.780 total time=   0.0s
[CV 4/5; 74/400] START max_depth=2, min_samples_leaf=4, n_estimators=4..........
[CV 4/5; 74/400] END max_depth=2, min_samples_leaf=4, n_estimators=4;, score=0.785 total time=   0.0s
[CV 5/5; 74/400] START max_depth=2, min_samples_leaf=4, n_estimators=4..........
[CV 5/5; 74/400] END max_depth=2, min_samples_leaf=4, n_estimators=4;, score=0.770 total time=   0.0s
[CV 1/5; 75/400] START max_depth=2, min_samples_leaf=4, n_estimators=5..........
[CV 1/5; 75/400] END max_depth=2, min_samples_leaf=4, n_estimators=5;, score=0.797 total time=   0.0s
[CV 2/5; 75/400] START max_depth=2, min_samples_leaf=4, n_estimators=5..........
[CV 2/5; 75/400] END max_depth=2, min_samples_leaf=4, n_estimators=5;, score=0.794 total time=   0.0s
[CV 3/5; 75/400] START max_depth=2, min_samples_leaf=4, n_estimators=5..........
[CV 3/5; 75/400] END max_depth=2, min_samples_leaf=4, n_estimators=5;, score=0.780 total time=   0.0s
[CV 4/5; 75/400] START max_depth=2, min_samples_leaf=4, n_estimators=5..........
[CV 4/5; 75/400] END max_depth=2, min_samples_leaf=4, n_estimators=5;, score=0.790 total time=   0.0s
[CV 5/5; 75/400] START max_depth=2, min_samples_leaf=4, n_estimators=5..........
[CV 5/5; 75/400] END max_depth=2, min_samples_leaf=4, n_estimators=5;, score=0.764 total time=   0.0s
[CV 1/5; 76/400] START max_depth=2, min_samples_leaf=4, n_estimators=6..........
[CV 1/5; 76/400] END max_depth=2, min_samples_leaf=4, n_estimators=6;, score=0.784 total time=   0.0s
[CV 2/5; 76/400] START max_depth=2, min_samples_leaf=4, n_estimators=6..........
[CV 2/5; 76/400] END max_depth=2, min_samples_leaf=4, n_estimators=6;, score=0.796 total time=   0.0s
[CV 3/5; 76/400] START max_depth=2, min_samples_leaf=4, n_estimators=6..........
[CV 3/5; 76/400] END max_depth=2, min_samples_leaf=4, n_estimators=6;, score=0.775 total time=   0.0s
[CV 4/5; 76/400] START max_depth=2, min_samples_leaf=4, n_estimators=6..........
[CV 4/5; 76/400] END max_depth=2, min_samples_leaf=4, n_estimators=6;, score=0.790 total time=   0.0s
[CV 5/5; 76/400] START max_depth=2, min_samples_leaf=4, n_estimators=6..........
[CV 5/5; 76/400] END max_depth=2, min_samples_leaf=4, n_estimators=6;, score=0.771 total time=   0.0s
[CV 1/5; 77/400] START max_depth=2, min_samples_leaf=4, n_estimators=7..........
[CV 1/5; 77/400] END max_depth=2, min_samples_leaf=4, n_estimators=7;, score=0.794 total time=   0.0s
[CV 2/5; 77/400] START max_depth=2, min_samples_leaf=4, n_estimators=7..........
[CV 2/5; 77/400] END max_depth=2, min_samples_leaf=4, n_estimators=7;, score=0.787 total time=   0.0s
[CV 3/5; 77/400] START max_depth=2, min_samples_leaf=4, n_estimators=7..........
[CV 3/5; 77/400] END max_depth=2, min_samples_leaf=4, n_estimators=7;, score=0.775 total time=   0.0s
[CV 4/5; 77/400] START max_depth=2, min_samples_leaf=4, n_estimators=7..........
[CV 4/5; 77/400] END max_depth=2, min_samples_leaf=4, n_estimators=7;, score=0.790 total time=   0.0s
[CV 5/5; 77/400] START max_depth=2, min_samples_leaf=4, n_estimators=7..........
[CV 5/5; 77/400] END max_depth=2, min_samples_leaf=4, n_estimators=7;, score=0.767 total time=   0.0s
[CV 1/5; 78/400] START max_depth=2, min_samples_leaf=4, n_estimators=8..........
[CV 1/5; 78/400] END max_depth=2, min_samples_leaf=4, n_estimators=8;, score=0.793 total time=   0.0s
[CV 2/5; 78/400] START max_depth=2, min_samples_leaf=4, n_estimators=8..........
[CV 2/5; 78/400] END max_depth=2, min_samples_leaf=4, n_estimators=8;, score=0.757 total time=   0.0s
[CV 3/5; 78/400] START max_depth=2, min_samples_leaf=4, n_estimators=8..........
[CV 3/5; 78/400] END max_depth=2, min_samples_leaf=4, n_estimators=8;, score=0.774 total time=   0.0s
[CV 4/5; 78/400] START max_depth=2, min_samples_leaf=4, n_estimators=8..........
[CV 4/5; 78/400] END max_depth=2, min_samples_leaf=4, n_estimators=8;, score=0.791 total time=   0.0s
[CV 5/5; 78/400] START max_depth=2, min_samples_leaf=4, n_estimators=8..........
[CV 5/5; 78/400] END max_depth=2, min_samples_leaf=4, n_estimators=8;, score=0.771 total time=   0.0s
[CV 1/5; 79/400] START max_depth=2, min_samples_leaf=4, n_estimators=9..........
[CV 1/5; 79/400] END max_depth=2, min_samples_leaf=4, n_estimators=9;, score=0.783 total time=   0.0s
[CV 2/5; 79/400] START max_depth=2, min_samples_leaf=4, n_estimators=9..........
[CV 2/5; 79/400] END max_depth=2, min_samples_leaf=4, n_estimators=9;, score=0.789 total time=   0.0s
[CV 3/5; 79/400] START max_depth=2, min_samples_leaf=4, n_estimators=9..........
[CV 3/5; 79/400] END max_depth=2, min_samples_leaf=4, n_estimators=9;, score=0.780 total time=   0.0s
[CV 4/5; 79/400] START max_depth=2, min_samples_leaf=4, n_estimators=9..........
[CV 4/5; 79/400] END max_depth=2, min_samples_leaf=4, n_estimators=9;, score=0.788 total time=   0.0s
[CV 5/5; 79/400] START max_depth=2, min_samples_leaf=4, n_estimators=9..........
[CV 5/5; 79/400] END max_depth=2, min_samples_leaf=4, n_estimators=9;, score=0.771 total time=   0.0s
[CV 1/5; 80/400] START max_depth=2, min_samples_leaf=4, n_estimators=10.........
[CV 1/5; 80/400] END max_depth=2, min_samples_leaf=4, n_estimators=10;, score=0.791 total time=   0.0s
[CV 2/5; 80/400] START max_depth=2, min_samples_leaf=4, n_estimators=10.........
[CV 2/5; 80/400] END max_depth=2, min_samples_leaf=4, n_estimators=10;, score=0.788 total time=   0.0s
[CV 3/5; 80/400] START max_depth=2, min_samples_leaf=4, n_estimators=10.........
[CV 3/5; 80/400] END max_depth=2, min_samples_leaf=4, n_estimators=10;, score=0.780 total time=   0.0s
[CV 4/5; 80/400] START max_depth=2, min_samples_leaf=4, n_estimators=10.........
[CV 4/5; 80/400] END max_depth=2, min_samples_leaf=4, n_estimators=10;, score=0.781 total time=   0.0s
[CV 5/5; 80/400] START max_depth=2, min_samples_leaf=4, n_estimators=10.........
[CV 5/5; 80/400] END max_depth=2, min_samples_leaf=4, n_estimators=10;, score=0.777 total time=   0.0s
[CV 1/5; 81/400] START max_depth=3, min_samples_leaf=1, n_estimators=1..........
[CV 1/5; 81/400] END max_depth=3, min_samples_leaf=1, n_estimators=1;, score=0.783 total time=   0.0s
[CV 2/5; 81/400] START max_depth=3, min_samples_leaf=1, n_estimators=1..........
[CV 2/5; 81/400] END max_depth=3, min_samples_leaf=1, n_estimators=1;, score=0.624 total time=   0.0s
[CV 3/5; 81/400] START max_depth=3, min_samples_leaf=1, n_estimators=1..........
[CV 3/5; 81/400] END max_depth=3, min_samples_leaf=1, n_estimators=1;, score=0.780 total time=   0.0s
[CV 4/5; 81/400] START max_depth=3, min_samples_leaf=1, n_estimators=1..........
[CV 4/5; 81/400] END max_depth=3, min_samples_leaf=1, n_estimators=1;, score=0.742 total time=   0.0s
[CV 5/5; 81/400] START max_depth=3, min_samples_leaf=1, n_estimators=1..........
[CV 5/5; 81/400] END max_depth=3, min_samples_leaf=1, n_estimators=1;, score=0.652 total time=   0.0s
[CV 1/5; 82/400] START max_depth=3, min_samples_leaf=1, n_estimators=2..........
[CV 1/5; 82/400] END max_depth=3, min_samples_leaf=1, n_estimators=2;, score=0.798 total time=   0.0s
[CV 2/5; 82/400] START max_depth=3, min_samples_leaf=1, n_estimators=2..........
[CV 2/5; 82/400] END max_depth=3, min_samples_leaf=1, n_estimators=2;, score=0.794 total time=   0.0s
[CV 3/5; 82/400] START max_depth=3, min_samples_leaf=1, n_estimators=2..........
[CV 3/5; 82/400] END max_depth=3, min_samples_leaf=1, n_estimators=2;, score=0.780 total time=   0.0s
[CV 4/5; 82/400] START max_depth=3, min_samples_leaf=1, n_estimators=2..........
[CV 4/5; 82/400] END max_depth=3, min_samples_leaf=1, n_estimators=2;, score=0.786 total time=   0.0s
[CV 5/5; 82/400] START max_depth=3, min_samples_leaf=1, n_estimators=2..........
[CV 5/5; 82/400] END max_depth=3, min_samples_leaf=1, n_estimators=2;, score=0.775 total time=   0.0s
[CV 1/5; 83/400] START max_depth=3, min_samples_leaf=1, n_estimators=3..........
[CV 1/5; 83/400] END max_depth=3, min_samples_leaf=1, n_estimators=3;, score=0.795 total time=   0.0s
[CV 2/5; 83/400] START max_depth=3, min_samples_leaf=1, n_estimators=3..........
[CV 2/5; 83/400] END max_depth=3, min_samples_leaf=1, n_estimators=3;, score=0.788 total time=   0.0s
[CV 3/5; 83/400] START max_depth=3, min_samples_leaf=1, n_estimators=3..........
[CV 3/5; 83/400] END max_depth=3, min_samples_leaf=1, n_estimators=3;, score=0.771 total time=   0.0s
[CV 4/5; 83/400] START max_depth=3, min_samples_leaf=1, n_estimators=3..........
[CV 4/5; 83/400] END max_depth=3, min_samples_leaf=1, n_estimators=3;, score=0.778 total time=   0.0s
[CV 5/5; 83/400] START max_depth=3, min_samples_leaf=1, n_estimators=3..........
[CV 5/5; 83/400] END max_depth=3, min_samples_leaf=1, n_estimators=3;, score=0.771 total time=   0.0s
[CV 1/5; 84/400] START max_depth=3, min_samples_leaf=1, n_estimators=4..........
[CV 1/5; 84/400] END max_depth=3, min_samples_leaf=1, n_estimators=4;, score=0.796 total time=   0.0s
[CV 2/5; 84/400] START max_depth=3, min_samples_leaf=1, n_estimators=4..........
[CV 2/5; 84/400] END max_depth=3, min_samples_leaf=1, n_estimators=4;, score=0.784 total time=   0.0s
[CV 3/5; 84/400] START max_depth=3, min_samples_leaf=1, n_estimators=4..........
[CV 3/5; 84/400] END max_depth=3, min_samples_leaf=1, n_estimators=4;, score=0.780 total time=   0.0s
[CV 4/5; 84/400] START max_depth=3, min_samples_leaf=1, n_estimators=4..........
[CV 4/5; 84/400] END max_depth=3, min_samples_leaf=1, n_estimators=4;, score=0.782 total time=   0.0s
[CV 5/5; 84/400] START max_depth=3, min_samples_leaf=1, n_estimators=4..........
[CV 5/5; 84/400] END max_depth=3, min_samples_leaf=1, n_estimators=4;, score=0.785 total time=   0.0s
[CV 1/5; 85/400] START max_depth=3, min_samples_leaf=1, n_estimators=5..........
[CV 1/5; 85/400] END max_depth=3, min_samples_leaf=1, n_estimators=5;, score=0.779 total time=   0.0s
[CV 2/5; 85/400] START max_depth=3, min_samples_leaf=1, n_estimators=5..........
[CV 2/5; 85/400] END max_depth=3, min_samples_leaf=1, n_estimators=5;, score=0.797 total time=   0.0s
[CV 3/5; 85/400] START max_depth=3, min_samples_leaf=1, n_estimators=5..........
[CV 3/5; 85/400] END max_depth=3, min_samples_leaf=1, n_estimators=5;, score=0.771 total time=   0.0s
[CV 4/5; 85/400] START max_depth=3, min_samples_leaf=1, n_estimators=5..........
[CV 4/5; 85/400] END max_depth=3, min_samples_leaf=1, n_estimators=5;, score=0.794 total time=   0.0s
[CV 5/5; 85/400] START max_depth=3, min_samples_leaf=1, n_estimators=5..........
[CV 5/5; 85/400] END max_depth=3, min_samples_leaf=1, n_estimators=5;, score=0.775 total time=   0.0s
[CV 1/5; 86/400] START max_depth=3, min_samples_leaf=1, n_estimators=6..........
[CV 1/5; 86/400] END max_depth=3, min_samples_leaf=1, n_estimators=6;, score=0.791 total time=   0.0s
[CV 2/5; 86/400] START max_depth=3, min_samples_leaf=1, n_estimators=6..........
[CV 2/5; 86/400] END max_depth=3, min_samples_leaf=1, n_estimators=6;, score=0.789 total time=   0.0s
[CV 3/5; 86/400] START max_depth=3, min_samples_leaf=1, n_estimators=6..........
[CV 3/5; 86/400] END max_depth=3, min_samples_leaf=1, n_estimators=6;, score=0.784 total time=   0.0s
[CV 4/5; 86/400] START max_depth=3, min_samples_leaf=1, n_estimators=6..........
[CV 4/5; 86/400] END max_depth=3, min_samples_leaf=1, n_estimators=6;, score=0.790 total time=   0.0s
[CV 5/5; 86/400] START max_depth=3, min_samples_leaf=1, n_estimators=6..........
[CV 5/5; 86/400] END max_depth=3, min_samples_leaf=1, n_estimators=6;, score=0.767 total time=   0.0s
[CV 1/5; 87/400] START max_depth=3, min_samples_leaf=1, n_estimators=7..........
[CV 1/5; 87/400] END max_depth=3, min_samples_leaf=1, n_estimators=7;, score=0.766 total time=   0.0s
[CV 2/5; 87/400] START max_depth=3, min_samples_leaf=1, n_estimators=7..........
[CV 2/5; 87/400] END max_depth=3, min_samples_leaf=1, n_estimators=7;, score=0.791 total time=   0.0s
[CV 3/5; 87/400] START max_depth=3, min_samples_leaf=1, n_estimators=7..........
[CV 3/5; 87/400] END max_depth=3, min_samples_leaf=1, n_estimators=7;, score=0.780 total time=   0.0s
[CV 4/5; 87/400] START max_depth=3, min_samples_leaf=1, n_estimators=7..........
[CV 4/5; 87/400] END max_depth=3, min_samples_leaf=1, n_estimators=7;, score=0.792 total time=   0.0s
[CV 5/5; 87/400] START max_depth=3, min_samples_leaf=1, n_estimators=7..........
[CV 5/5; 87/400] END max_depth=3, min_samples_leaf=1, n_estimators=7;, score=0.775 total time=   0.0s
[CV 1/5; 88/400] START max_depth=3, min_samples_leaf=1, n_estimators=8..........
[CV 1/5; 88/400] END max_depth=3, min_samples_leaf=1, n_estimators=8;, score=0.788 total time=   0.0s
[CV 2/5; 88/400] START max_depth=3, min_samples_leaf=1, n_estimators=8..........
[CV 2/5; 88/400] END max_depth=3, min_samples_leaf=1, n_estimators=8;, score=0.785 total time=   0.0s
[CV 3/5; 88/400] START max_depth=3, min_samples_leaf=1, n_estimators=8..........
[CV 3/5; 88/400] END max_depth=3, min_samples_leaf=1, n_estimators=8;, score=0.781 total time=   0.0s
[CV 4/5; 88/400] START max_depth=3, min_samples_leaf=1, n_estimators=8..........
[CV 4/5; 88/400] END max_depth=3, min_samples_leaf=1, n_estimators=8;, score=0.791 total time=   0.0s
[CV 5/5; 88/400] START max_depth=3, min_samples_leaf=1, n_estimators=8..........
[CV 5/5; 88/400] END max_depth=3, min_samples_leaf=1, n_estimators=8;, score=0.763 total time=   0.0s
[CV 1/5; 89/400] START max_depth=3, min_samples_leaf=1, n_estimators=9..........
[CV 1/5; 89/400] END max_depth=3, min_samples_leaf=1, n_estimators=9;, score=0.797 total time=   0.0s
[CV 2/5; 89/400] START max_depth=3, min_samples_leaf=1, n_estimators=9..........
[CV 2/5; 89/400] END max_depth=3, min_samples_leaf=1, n_estimators=9;, score=0.788 total time=   0.0s
[CV 3/5; 89/400] START max_depth=3, min_samples_leaf=1, n_estimators=9..........
[CV 3/5; 89/400] END max_depth=3, min_samples_leaf=1, n_estimators=9;, score=0.784 total time=   0.0s
[CV 4/5; 89/400] START max_depth=3, min_samples_leaf=1, n_estimators=9..........
[CV 4/5; 89/400] END max_depth=3, min_samples_leaf=1, n_estimators=9;, score=0.791 total time=   0.0s
[CV 5/5; 89/400] START max_depth=3, min_samples_leaf=1, n_estimators=9..........
[CV 5/5; 89/400] END max_depth=3, min_samples_leaf=1, n_estimators=9;, score=0.777 total time=   0.0s
[CV 1/5; 90/400] START max_depth=3, min_samples_leaf=1, n_estimators=10.........
[CV 1/5; 90/400] END max_depth=3, min_samples_leaf=1, n_estimators=10;, score=0.797 total time=   0.0s
[CV 2/5; 90/400] START max_depth=3, min_samples_leaf=1, n_estimators=10.........
[CV 2/5; 90/400] END max_depth=3, min_samples_leaf=1, n_estimators=10;, score=0.788 total time=   0.0s
[CV 3/5; 90/400] START max_depth=3, min_samples_leaf=1, n_estimators=10.........
[CV 3/5; 90/400] END max_depth=3, min_samples_leaf=1, n_estimators=10;, score=0.777 total time=   0.0s
[CV 4/5; 90/400] START max_depth=3, min_samples_leaf=1, n_estimators=10.........
[CV 4/5; 90/400] END max_depth=3, min_samples_leaf=1, n_estimators=10;, score=0.796 total time=   0.0s
[CV 5/5; 90/400] START max_depth=3, min_samples_leaf=1, n_estimators=10.........
[CV 5/5; 90/400] END max_depth=3, min_samples_leaf=1, n_estimators=10;, score=0.777 total time=   0.0s
[CV 1/5; 91/400] START max_depth=3, min_samples_leaf=2, n_estimators=1..........
[CV 1/5; 91/400] END max_depth=3, min_samples_leaf=2, n_estimators=1;, score=0.769 total time=   0.0s
[CV 2/5; 91/400] START max_depth=3, min_samples_leaf=2, n_estimators=1..........
[CV 2/5; 91/400] END max_depth=3, min_samples_leaf=2, n_estimators=1;, score=0.774 total time=   0.0s
[CV 3/5; 91/400] START max_depth=3, min_samples_leaf=2, n_estimators=1..........
[CV 3/5; 91/400] END max_depth=3, min_samples_leaf=2, n_estimators=1;, score=0.683 total time=   0.0s
[CV 4/5; 91/400] START max_depth=3, min_samples_leaf=2, n_estimators=1..........
[CV 4/5; 91/400] END max_depth=3, min_samples_leaf=2, n_estimators=1;, score=0.764 total time=   0.0s
[CV 5/5; 91/400] START max_depth=3, min_samples_leaf=2, n_estimators=1..........
[CV 5/5; 91/400] END max_depth=3, min_samples_leaf=2, n_estimators=1;, score=0.625 total time=   0.0s
[CV 1/5; 92/400] START max_depth=3, min_samples_leaf=2, n_estimators=2..........
[CV 1/5; 92/400] END max_depth=3, min_samples_leaf=2, n_estimators=2;, score=0.793 total time=   0.0s
[CV 2/5; 92/400] START max_depth=3, min_samples_leaf=2, n_estimators=2..........
[CV 2/5; 92/400] END max_depth=3, min_samples_leaf=2, n_estimators=2;, score=0.783 total time=   0.0s
[CV 3/5; 92/400] START max_depth=3, min_samples_leaf=2, n_estimators=2..........
[CV 3/5; 92/400] END max_depth=3, min_samples_leaf=2, n_estimators=2;, score=0.776 total time=   0.0s
[CV 4/5; 92/400] START max_depth=3, min_samples_leaf=2, n_estimators=2..........
[CV 4/5; 92/400] END max_depth=3, min_samples_leaf=2, n_estimators=2;, score=0.788 total time=   0.0s
[CV 5/5; 92/400] START max_depth=3, min_samples_leaf=2, n_estimators=2..........
[CV 5/5; 92/400] END max_depth=3, min_samples_leaf=2, n_estimators=2;, score=0.680 total time=   0.0s
[CV 1/5; 93/400] START max_depth=3, min_samples_leaf=2, n_estimators=3..........
[CV 1/5; 93/400] END max_depth=3, min_samples_leaf=2, n_estimators=3;, score=0.768 total time=   0.0s
[CV 2/5; 93/400] START max_depth=3, min_samples_leaf=2, n_estimators=3..........
[CV 2/5; 93/400] END max_depth=3, min_samples_leaf=2, n_estimators=3;, score=0.794 total time=   0.0s
[CV 3/5; 93/400] START max_depth=3, min_samples_leaf=2, n_estimators=3..........
[CV 3/5; 93/400] END max_depth=3, min_samples_leaf=2, n_estimators=3;, score=0.769 total time=   0.0s
[CV 4/5; 93/400] START max_depth=3, min_samples_leaf=2, n_estimators=3..........
[CV 4/5; 93/400] END max_depth=3, min_samples_leaf=2, n_estimators=3;, score=0.791 total time=   0.0s
[CV 5/5; 93/400] START max_depth=3, min_samples_leaf=2, n_estimators=3..........
[CV 5/5; 93/400] END max_depth=3, min_samples_leaf=2, n_estimators=3;, score=0.781 total time=   0.0s
[CV 1/5; 94/400] START max_depth=3, min_samples_leaf=2, n_estimators=4..........
[CV 1/5; 94/400] END max_depth=3, min_samples_leaf=2, n_estimators=4;, score=0.785 total time=   0.0s
[CV 2/5; 94/400] START max_depth=3, min_samples_leaf=2, n_estimators=4..........
[CV 2/5; 94/400] END max_depth=3, min_samples_leaf=2, n_estimators=4;, score=0.795 total time=   0.0s
[CV 3/5; 94/400] START max_depth=3, min_samples_leaf=2, n_estimators=4..........
[CV 3/5; 94/400] END max_depth=3, min_samples_leaf=2, n_estimators=4;, score=0.778 total time=   0.0s
[CV 4/5; 94/400] START max_depth=3, min_samples_leaf=2, n_estimators=4..........
[CV 4/5; 94/400] END max_depth=3, min_samples_leaf=2, n_estimators=4;, score=0.788 total time=   0.0s
[CV 5/5; 94/400] START max_depth=3, min_samples_leaf=2, n_estimators=4..........
[CV 5/5; 94/400] END max_depth=3, min_samples_leaf=2, n_estimators=4;, score=0.780 total time=   0.0s
[CV 1/5; 95/400] START max_depth=3, min_samples_leaf=2, n_estimators=5..........
[CV 1/5; 95/400] END max_depth=3, min_samples_leaf=2, n_estimators=5;, score=0.796 total time=   0.0s
[CV 2/5; 95/400] START max_depth=3, min_samples_leaf=2, n_estimators=5..........
[CV 2/5; 95/400] END max_depth=3, min_samples_leaf=2, n_estimators=5;, score=0.791 total time=   0.0s
[CV 3/5; 95/400] START max_depth=3, min_samples_leaf=2, n_estimators=5..........
[CV 3/5; 95/400] END max_depth=3, min_samples_leaf=2, n_estimators=5;, score=0.775 total time=   0.0s
[CV 4/5; 95/400] START max_depth=3, min_samples_leaf=2, n_estimators=5..........
[CV 4/5; 95/400] END max_depth=3, min_samples_leaf=2, n_estimators=5;, score=0.793 total time=   0.0s
[CV 5/5; 95/400] START max_depth=3, min_samples_leaf=2, n_estimators=5..........
[CV 5/5; 95/400] END max_depth=3, min_samples_leaf=2, n_estimators=5;, score=0.775 total time=   0.0s
[CV 1/5; 96/400] START max_depth=3, min_samples_leaf=2, n_estimators=6..........
[CV 1/5; 96/400] END max_depth=3, min_samples_leaf=2, n_estimators=6;, score=0.788 total time=   0.0s
[CV 2/5; 96/400] START max_depth=3, min_samples_leaf=2, n_estimators=6..........
[CV 2/5; 96/400] END max_depth=3, min_samples_leaf=2, n_estimators=6;, score=0.785 total time=   0.0s
[CV 3/5; 96/400] START max_depth=3, min_samples_leaf=2, n_estimators=6..........
[CV 3/5; 96/400] END max_depth=3, min_samples_leaf=2, n_estimators=6;, score=0.777 total time=   0.0s
[CV 4/5; 96/400] START max_depth=3, min_samples_leaf=2, n_estimators=6..........
[CV 4/5; 96/400] END max_depth=3, min_samples_leaf=2, n_estimators=6;, score=0.789 total time=   0.0s
[CV 5/5; 96/400] START max_depth=3, min_samples_leaf=2, n_estimators=6..........
[CV 5/5; 96/400] END max_depth=3, min_samples_leaf=2, n_estimators=6;, score=0.774 total time=   0.0s
[CV 1/5; 97/400] START max_depth=3, min_samples_leaf=2, n_estimators=7..........
[CV 1/5; 97/400] END max_depth=3, min_samples_leaf=2, n_estimators=7;, score=0.794 total time=   0.0s
[CV 2/5; 97/400] START max_depth=3, min_samples_leaf=2, n_estimators=7..........
[CV 2/5; 97/400] END max_depth=3, min_samples_leaf=2, n_estimators=7;, score=0.792 total time=   0.0s
[CV 3/5; 97/400] START max_depth=3, min_samples_leaf=2, n_estimators=7..........
[CV 3/5; 97/400] END max_depth=3, min_samples_leaf=2, n_estimators=7;, score=0.782 total time=   0.0s
[CV 4/5; 97/400] START max_depth=3, min_samples_leaf=2, n_estimators=7..........
[CV 4/5; 97/400] END max_depth=3, min_samples_leaf=2, n_estimators=7;, score=0.801 total time=   0.0s
[CV 5/5; 97/400] START max_depth=3, min_samples_leaf=2, n_estimators=7..........
[CV 5/5; 97/400] END max_depth=3, min_samples_leaf=2, n_estimators=7;, score=0.771 total time=   0.0s
[CV 1/5; 98/400] START max_depth=3, min_samples_leaf=2, n_estimators=8..........
[CV 1/5; 98/400] END max_depth=3, min_samples_leaf=2, n_estimators=8;, score=0.796 total time=   0.0s
[CV 2/5; 98/400] START max_depth=3, min_samples_leaf=2, n_estimators=8..........
[CV 2/5; 98/400] END max_depth=3, min_samples_leaf=2, n_estimators=8;, score=0.794 total time=   0.0s
[CV 3/5; 98/400] START max_depth=3, min_samples_leaf=2, n_estimators=8..........
[CV 3/5; 98/400] END max_depth=3, min_samples_leaf=2, n_estimators=8;, score=0.778 total time=   0.0s
[CV 4/5; 98/400] START max_depth=3, min_samples_leaf=2, n_estimators=8..........
[CV 4/5; 98/400] END max_depth=3, min_samples_leaf=2, n_estimators=8;, score=0.791 total time=   0.0s
[CV 5/5; 98/400] START max_depth=3, min_samples_leaf=2, n_estimators=8..........
[CV 5/5; 98/400] END max_depth=3, min_samples_leaf=2, n_estimators=8;, score=0.769 total time=   0.0s
[CV 1/5; 99/400] START max_depth=3, min_samples_leaf=2, n_estimators=9..........
[CV 1/5; 99/400] END max_depth=3, min_samples_leaf=2, n_estimators=9;, score=0.796 total time=   0.0s
[CV 2/5; 99/400] START max_depth=3, min_samples_leaf=2, n_estimators=9..........
[CV 2/5; 99/400] END max_depth=3, min_samples_leaf=2, n_estimators=9;, score=0.794 total time=   0.1s
[CV 3/5; 99/400] START max_depth=3, min_samples_leaf=2, n_estimators=9..........
[CV 3/5; 99/400] END max_depth=3, min_samples_leaf=2, n_estimators=9;, score=0.783 total time=   0.1s
[CV 4/5; 99/400] START max_depth=3, min_samples_leaf=2, n_estimators=9..........
[CV 4/5; 99/400] END max_depth=3, min_samples_leaf=2, n_estimators=9;, score=0.792 total time=   0.1s
[CV 5/5; 99/400] START max_depth=3, min_samples_leaf=2, n_estimators=9..........
[CV 5/5; 99/400] END max_depth=3, min_samples_leaf=2, n_estimators=9;, score=0.777 total time=   0.1s
[CV 1/5; 100/400] START max_depth=3, min_samples_leaf=2, n_estimators=10........
[CV 1/5; 100/400] END max_depth=3, min_samples_leaf=2, n_estimators=10;, score=0.799 total time=   0.1s
[CV 2/5; 100/400] START max_depth=3, min_samples_leaf=2, n_estimators=10........
[CV 2/5; 100/400] END max_depth=3, min_samples_leaf=2, n_estimators=10;, score=0.789 total time=   0.1s
[CV 3/5; 100/400] START max_depth=3, min_samples_leaf=2, n_estimators=10........
[CV 3/5; 100/400] END max_depth=3, min_samples_leaf=2, n_estimators=10;, score=0.778 total time=   0.1s
[CV 4/5; 100/400] START max_depth=3, min_samples_leaf=2, n_estimators=10........
[CV 4/5; 100/400] END max_depth=3, min_samples_leaf=2, n_estimators=10;, score=0.792 total time=   0.1s
[CV 5/5; 100/400] START max_depth=3, min_samples_leaf=2, n_estimators=10........
[CV 5/5; 100/400] END max_depth=3, min_samples_leaf=2, n_estimators=10;, score=0.778 total time=   0.1s
[CV 1/5; 101/400] START max_depth=3, min_samples_leaf=3, n_estimators=1.........
[CV 1/5; 101/400] END max_depth=3, min_samples_leaf=3, n_estimators=1;, score=0.641 total time=   0.0s
[CV 2/5; 101/400] START max_depth=3, min_samples_leaf=3, n_estimators=1.........
[CV 2/5; 101/400] END max_depth=3, min_samples_leaf=3, n_estimators=1;, score=0.736 total time=   0.0s
[CV 3/5; 101/400] START max_depth=3, min_samples_leaf=3, n_estimators=1.........
[CV 3/5; 101/400] END max_depth=3, min_samples_leaf=3, n_estimators=1;, score=0.654 total time=   0.0s
[CV 4/5; 101/400] START max_depth=3, min_samples_leaf=3, n_estimators=1.........
[CV 4/5; 101/400] END max_depth=3, min_samples_leaf=3, n_estimators=1;, score=0.626 total time=   0.0s
[CV 5/5; 101/400] START max_depth=3, min_samples_leaf=3, n_estimators=1.........
[CV 5/5; 101/400] END max_depth=3, min_samples_leaf=3, n_estimators=1;, score=0.769 total time=   0.0s
[CV 1/5; 102/400] START max_depth=3, min_samples_leaf=3, n_estimators=2.........
[CV 1/5; 102/400] END max_depth=3, min_samples_leaf=3, n_estimators=2;, score=0.666 total time=   0.0s
[CV 2/5; 102/400] START max_depth=3, min_samples_leaf=3, n_estimators=2.........
[CV 2/5; 102/400] END max_depth=3, min_samples_leaf=3, n_estimators=2;, score=0.793 total time=   0.0s
[CV 3/5; 102/400] START max_depth=3, min_samples_leaf=3, n_estimators=2.........
[CV 3/5; 102/400] END max_depth=3, min_samples_leaf=3, n_estimators=2;, score=0.786 total time=   0.0s
[CV 4/5; 102/400] START max_depth=3, min_samples_leaf=3, n_estimators=2.........
[CV 4/5; 102/400] END max_depth=3, min_samples_leaf=3, n_estimators=2;, score=0.775 total time=   0.0s
[CV 5/5; 102/400] START max_depth=3, min_samples_leaf=3, n_estimators=2.........
[CV 5/5; 102/400] END max_depth=3, min_samples_leaf=3, n_estimators=2;, score=0.762 total time=   0.0s
[CV 1/5; 103/400] START max_depth=3, min_samples_leaf=3, n_estimators=3.........
[CV 1/5; 103/400] END max_depth=3, min_samples_leaf=3, n_estimators=3;, score=0.792 total time=   0.0s
[CV 2/5; 103/400] START max_depth=3, min_samples_leaf=3, n_estimators=3.........
[CV 2/5; 103/400] END max_depth=3, min_samples_leaf=3, n_estimators=3;, score=0.785 total time=   0.0s
[CV 3/5; 103/400] START max_depth=3, min_samples_leaf=3, n_estimators=3.........
[CV 3/5; 103/400] END max_depth=3, min_samples_leaf=3, n_estimators=3;, score=0.776 total time=   0.0s
[CV 4/5; 103/400] START max_depth=3, min_samples_leaf=3, n_estimators=3.........
[CV 4/5; 103/400] END max_depth=3, min_samples_leaf=3, n_estimators=3;, score=0.793 total time=   0.0s
[CV 5/5; 103/400] START max_depth=3, min_samples_leaf=3, n_estimators=3.........
[CV 5/5; 103/400] END max_depth=3, min_samples_leaf=3, n_estimators=3;, score=0.780 total time=   0.0s
[CV 1/5; 104/400] START max_depth=3, min_samples_leaf=3, n_estimators=4.........
[CV 1/5; 104/400] END max_depth=3, min_samples_leaf=3, n_estimators=4;, score=0.796 total time=   0.0s
[CV 2/5; 104/400] START max_depth=3, min_samples_leaf=3, n_estimators=4.........
[CV 2/5; 104/400] END max_depth=3, min_samples_leaf=3, n_estimators=4;, score=0.788 total time=   0.0s
[CV 3/5; 104/400] START max_depth=3, min_samples_leaf=3, n_estimators=4.........
[CV 3/5; 104/400] END max_depth=3, min_samples_leaf=3, n_estimators=4;, score=0.781 total time=   0.0s
[CV 4/5; 104/400] START max_depth=3, min_samples_leaf=3, n_estimators=4.........
[CV 4/5; 104/400] END max_depth=3, min_samples_leaf=3, n_estimators=4;, score=0.789 total time=   0.0s
[CV 5/5; 104/400] START max_depth=3, min_samples_leaf=3, n_estimators=4.........
[CV 5/5; 104/400] END max_depth=3, min_samples_leaf=3, n_estimators=4;, score=0.780 total time=   0.0s
[CV 1/5; 105/400] START max_depth=3, min_samples_leaf=3, n_estimators=5.........
[CV 1/5; 105/400] END max_depth=3, min_samples_leaf=3, n_estimators=5;, score=0.798 total time=   0.0s
[CV 2/5; 105/400] START max_depth=3, min_samples_leaf=3, n_estimators=5.........
[CV 2/5; 105/400] END max_depth=3, min_samples_leaf=3, n_estimators=5;, score=0.790 total time=   0.0s
[CV 3/5; 105/400] START max_depth=3, min_samples_leaf=3, n_estimators=5.........
[CV 3/5; 105/400] END max_depth=3, min_samples_leaf=3, n_estimators=5;, score=0.786 total time=   0.0s
[CV 4/5; 105/400] START max_depth=3, min_samples_leaf=3, n_estimators=5.........
[CV 4/5; 105/400] END max_depth=3, min_samples_leaf=3, n_estimators=5;, score=0.794 total time=   0.0s
[CV 5/5; 105/400] START max_depth=3, min_samples_leaf=3, n_estimators=5.........
[CV 5/5; 105/400] END max_depth=3, min_samples_leaf=3, n_estimators=5;, score=0.775 total time=   0.0s
[CV 1/5; 106/400] START max_depth=3, min_samples_leaf=3, n_estimators=6.........
[CV 1/5; 106/400] END max_depth=3, min_samples_leaf=3, n_estimators=6;, score=0.798 total time=   0.0s
[CV 2/5; 106/400] START max_depth=3, min_samples_leaf=3, n_estimators=6.........
[CV 2/5; 106/400] END max_depth=3, min_samples_leaf=3, n_estimators=6;, score=0.796 total time=   0.0s
[CV 3/5; 106/400] START max_depth=3, min_samples_leaf=3, n_estimators=6.........
[CV 3/5; 106/400] END max_depth=3, min_samples_leaf=3, n_estimators=6;, score=0.783 total time=   0.0s
[CV 4/5; 106/400] START max_depth=3, min_samples_leaf=3, n_estimators=6.........
[CV 4/5; 106/400] END max_depth=3, min_samples_leaf=3, n_estimators=6;, score=0.792 total time=   0.0s
[CV 5/5; 106/400] START max_depth=3, min_samples_leaf=3, n_estimators=6.........
[CV 5/5; 106/400] END max_depth=3, min_samples_leaf=3, n_estimators=6;, score=0.778 total time=   0.0s
[CV 1/5; 107/400] START max_depth=3, min_samples_leaf=3, n_estimators=7.........
[CV 1/5; 107/400] END max_depth=3, min_samples_leaf=3, n_estimators=7;, score=0.781 total time=   0.0s
[CV 2/5; 107/400] START max_depth=3, min_samples_leaf=3, n_estimators=7.........
[CV 2/5; 107/400] END max_depth=3, min_samples_leaf=3, n_estimators=7;, score=0.785 total time=   0.0s
[CV 3/5; 107/400] START max_depth=3, min_samples_leaf=3, n_estimators=7.........
[CV 3/5; 107/400] END max_depth=3, min_samples_leaf=3, n_estimators=7;, score=0.783 total time=   0.0s
[CV 4/5; 107/400] START max_depth=3, min_samples_leaf=3, n_estimators=7.........
[CV 4/5; 107/400] END max_depth=3, min_samples_leaf=3, n_estimators=7;, score=0.792 total time=   0.0s
[CV 5/5; 107/400] START max_depth=3, min_samples_leaf=3, n_estimators=7.........
[CV 5/5; 107/400] END max_depth=3, min_samples_leaf=3, n_estimators=7;, score=0.775 total time=   0.1s
[CV 1/5; 108/400] START max_depth=3, min_samples_leaf=3, n_estimators=8.........
[CV 1/5; 108/400] END max_depth=3, min_samples_leaf=3, n_estimators=8;, score=0.785 total time=   0.1s
[CV 2/5; 108/400] START max_depth=3, min_samples_leaf=3, n_estimators=8.........
[CV 2/5; 108/400] END max_depth=3, min_samples_leaf=3, n_estimators=8;, score=0.786 total time=   0.1s
[CV 3/5; 108/400] START max_depth=3, min_samples_leaf=3, n_estimators=8.........
[CV 3/5; 108/400] END max_depth=3, min_samples_leaf=3, n_estimators=8;, score=0.782 total time=   0.1s
[CV 4/5; 108/400] START max_depth=3, min_samples_leaf=3, n_estimators=8.........
[CV 4/5; 108/400] END max_depth=3, min_samples_leaf=3, n_estimators=8;, score=0.791 total time=   0.1s
[CV 5/5; 108/400] START max_depth=3, min_samples_leaf=3, n_estimators=8.........
[CV 5/5; 108/400] END max_depth=3, min_samples_leaf=3, n_estimators=8;, score=0.782 total time=   0.1s
[CV 1/5; 109/400] START max_depth=3, min_samples_leaf=3, n_estimators=9.........
[CV 1/5; 109/400] END max_depth=3, min_samples_leaf=3, n_estimators=9;, score=0.799 total time=   0.1s
[CV 2/5; 109/400] START max_depth=3, min_samples_leaf=3, n_estimators=9.........
[CV 2/5; 109/400] END max_depth=3, min_samples_leaf=3, n_estimators=9;, score=0.788 total time=   0.1s
[CV 3/5; 109/400] START max_depth=3, min_samples_leaf=3, n_estimators=9.........
[CV 3/5; 109/400] END max_depth=3, min_samples_leaf=3, n_estimators=9;, score=0.778 total time=   0.1s
[CV 4/5; 109/400] START max_depth=3, min_samples_leaf=3, n_estimators=9.........
[CV 4/5; 109/400] END max_depth=3, min_samples_leaf=3, n_estimators=9;, score=0.791 total time=   0.1s
[CV 5/5; 109/400] START max_depth=3, min_samples_leaf=3, n_estimators=9.........
[CV 5/5; 109/400] END max_depth=3, min_samples_leaf=3, n_estimators=9;, score=0.774 total time=   0.1s
[CV 1/5; 110/400] START max_depth=3, min_samples_leaf=3, n_estimators=10........
[CV 1/5; 110/400] END max_depth=3, min_samples_leaf=3, n_estimators=10;, score=0.776 total time=   0.1s
[CV 2/5; 110/400] START max_depth=3, min_samples_leaf=3, n_estimators=10........
[CV 2/5; 110/400] END max_depth=3, min_samples_leaf=3, n_estimators=10;, score=0.788 total time=   0.1s
[CV 3/5; 110/400] START max_depth=3, min_samples_leaf=3, n_estimators=10........
[CV 3/5; 110/400] END max_depth=3, min_samples_leaf=3, n_estimators=10;, score=0.778 total time=   0.1s
[CV 4/5; 110/400] START max_depth=3, min_samples_leaf=3, n_estimators=10........
[CV 4/5; 110/400] END max_depth=3, min_samples_leaf=3, n_estimators=10;, score=0.792 total time=   0.1s
[CV 5/5; 110/400] START max_depth=3, min_samples_leaf=3, n_estimators=10........
[CV 5/5; 110/400] END max_depth=3, min_samples_leaf=3, n_estimators=10;, score=0.776 total time=   0.1s
[CV 1/5; 111/400] START max_depth=3, min_samples_leaf=4, n_estimators=1.........
[CV 1/5; 111/400] END max_depth=3, min_samples_leaf=4, n_estimators=1;, score=0.802 total time=   0.0s
[CV 2/5; 111/400] START max_depth=3, min_samples_leaf=4, n_estimators=1.........
[CV 2/5; 111/400] END max_depth=3, min_samples_leaf=4, n_estimators=1;, score=0.799 total time=   0.0s
[CV 3/5; 111/400] START max_depth=3, min_samples_leaf=4, n_estimators=1.........
[CV 3/5; 111/400] END max_depth=3, min_samples_leaf=4, n_estimators=1;, score=0.752 total time=   0.0s
[CV 4/5; 111/400] START max_depth=3, min_samples_leaf=4, n_estimators=1.........
[CV 4/5; 111/400] END max_depth=3, min_samples_leaf=4, n_estimators=1;, score=0.779 total time=   0.0s
[CV 5/5; 111/400] START max_depth=3, min_samples_leaf=4, n_estimators=1.........
[CV 5/5; 111/400] END max_depth=3, min_samples_leaf=4, n_estimators=1;, score=0.590 total time=   0.0s
[CV 1/5; 112/400] START max_depth=3, min_samples_leaf=4, n_estimators=2.........
[CV 1/5; 112/400] END max_depth=3, min_samples_leaf=4, n_estimators=2;, score=0.794 total time=   0.0s
[CV 2/5; 112/400] START max_depth=3, min_samples_leaf=4, n_estimators=2.........
[CV 2/5; 112/400] END max_depth=3, min_samples_leaf=4, n_estimators=2;, score=0.795 total time=   0.0s
[CV 3/5; 112/400] START max_depth=3, min_samples_leaf=4, n_estimators=2.........
[CV 3/5; 112/400] END max_depth=3, min_samples_leaf=4, n_estimators=2;, score=0.757 total time=   0.0s
[CV 4/5; 112/400] START max_depth=3, min_samples_leaf=4, n_estimators=2.........
[CV 4/5; 112/400] END max_depth=3, min_samples_leaf=4, n_estimators=2;, score=0.785 total time=   0.0s
[CV 5/5; 112/400] START max_depth=3, min_samples_leaf=4, n_estimators=2.........
[CV 5/5; 112/400] END max_depth=3, min_samples_leaf=4, n_estimators=2;, score=0.774 total time=   0.0s
[CV 1/5; 113/400] START max_depth=3, min_samples_leaf=4, n_estimators=3.........
[CV 1/5; 113/400] END max_depth=3, min_samples_leaf=4, n_estimators=3;, score=0.776 total time=   0.0s
[CV 2/5; 113/400] START max_depth=3, min_samples_leaf=4, n_estimators=3.........
[CV 2/5; 113/400] END max_depth=3, min_samples_leaf=4, n_estimators=3;, score=0.793 total time=   0.0s
[CV 3/5; 113/400] START max_depth=3, min_samples_leaf=4, n_estimators=3.........
[CV 3/5; 113/400] END max_depth=3, min_samples_leaf=4, n_estimators=3;, score=0.782 total time=   0.0s
[CV 4/5; 113/400] START max_depth=3, min_samples_leaf=4, n_estimators=3.........
[CV 4/5; 113/400] END max_depth=3, min_samples_leaf=4, n_estimators=3;, score=0.783 total time=   0.0s
[CV 5/5; 113/400] START max_depth=3, min_samples_leaf=4, n_estimators=3.........
[CV 5/5; 113/400] END max_depth=3, min_samples_leaf=4, n_estimators=3;, score=0.684 total time=   0.0s
[CV 1/5; 114/400] START max_depth=3, min_samples_leaf=4, n_estimators=4.........
[CV 1/5; 114/400] END max_depth=3, min_samples_leaf=4, n_estimators=4;, score=0.799 total time=   0.0s
[CV 2/5; 114/400] START max_depth=3, min_samples_leaf=4, n_estimators=4.........
[CV 2/5; 114/400] END max_depth=3, min_samples_leaf=4, n_estimators=4;, score=0.781 total time=   0.0s
[CV 3/5; 114/400] START max_depth=3, min_samples_leaf=4, n_estimators=4.........
[CV 3/5; 114/400] END max_depth=3, min_samples_leaf=4, n_estimators=4;, score=0.780 total time=   0.0s
[CV 4/5; 114/400] START max_depth=3, min_samples_leaf=4, n_estimators=4.........
[CV 4/5; 114/400] END max_depth=3, min_samples_leaf=4, n_estimators=4;, score=0.793 total time=   0.0s
[CV 5/5; 114/400] START max_depth=3, min_samples_leaf=4, n_estimators=4.........
[CV 5/5; 114/400] END max_depth=3, min_samples_leaf=4, n_estimators=4;, score=0.775 total time=   0.0s
[CV 1/5; 115/400] START max_depth=3, min_samples_leaf=4, n_estimators=5.........
[CV 1/5; 115/400] END max_depth=3, min_samples_leaf=4, n_estimators=5;, score=0.791 total time=   0.0s
[CV 2/5; 115/400] START max_depth=3, min_samples_leaf=4, n_estimators=5.........
[CV 2/5; 115/400] END max_depth=3, min_samples_leaf=4, n_estimators=5;, score=0.794 total time=   0.0s
[CV 3/5; 115/400] START max_depth=3, min_samples_leaf=4, n_estimators=5.........
[CV 3/5; 115/400] END max_depth=3, min_samples_leaf=4, n_estimators=5;, score=0.787 total time=   0.0s
[CV 4/5; 115/400] START max_depth=3, min_samples_leaf=4, n_estimators=5.........
[CV 4/5; 115/400] END max_depth=3, min_samples_leaf=4, n_estimators=5;, score=0.785 total time=   0.0s
[CV 5/5; 115/400] START max_depth=3, min_samples_leaf=4, n_estimators=5.........
[CV 5/5; 115/400] END max_depth=3, min_samples_leaf=4, n_estimators=5;, score=0.769 total time=   0.0s
[CV 1/5; 116/400] START max_depth=3, min_samples_leaf=4, n_estimators=6.........
[CV 1/5; 116/400] END max_depth=3, min_samples_leaf=4, n_estimators=6;, score=0.795 total time=   0.0s
[CV 2/5; 116/400] START max_depth=3, min_samples_leaf=4, n_estimators=6.........
[CV 2/5; 116/400] END max_depth=3, min_samples_leaf=4, n_estimators=6;, score=0.792 total time=   0.0s
[CV 3/5; 116/400] START max_depth=3, min_samples_leaf=4, n_estimators=6.........
[CV 3/5; 116/400] END max_depth=3, min_samples_leaf=4, n_estimators=6;, score=0.778 total time=   0.0s
[CV 4/5; 116/400] START max_depth=3, min_samples_leaf=4, n_estimators=6.........
[CV 4/5; 116/400] END max_depth=3, min_samples_leaf=4, n_estimators=6;, score=0.791 total time=   0.0s
[CV 5/5; 116/400] START max_depth=3, min_samples_leaf=4, n_estimators=6.........
[CV 5/5; 116/400] END max_depth=3, min_samples_leaf=4, n_estimators=6;, score=0.769 total time=   0.0s
[CV 1/5; 117/400] START max_depth=3, min_samples_leaf=4, n_estimators=7.........
[CV 1/5; 117/400] END max_depth=3, min_samples_leaf=4, n_estimators=7;, score=0.792 total time=   0.0s
[CV 2/5; 117/400] START max_depth=3, min_samples_leaf=4, n_estimators=7.........
[CV 2/5; 117/400] END max_depth=3, min_samples_leaf=4, n_estimators=7;, score=0.788 total time=   0.0s
[CV 3/5; 117/400] START max_depth=3, min_samples_leaf=4, n_estimators=7.........
[CV 3/5; 117/400] END max_depth=3, min_samples_leaf=4, n_estimators=7;, score=0.780 total time=   0.0s
[CV 4/5; 117/400] START max_depth=3, min_samples_leaf=4, n_estimators=7.........
[CV 4/5; 117/400] END max_depth=3, min_samples_leaf=4, n_estimators=7;, score=0.790 total time=   0.0s
[CV 5/5; 117/400] START max_depth=3, min_samples_leaf=4, n_estimators=7.........
[CV 5/5; 117/400] END max_depth=3, min_samples_leaf=4, n_estimators=7;, score=0.772 total time=   0.0s
[CV 1/5; 118/400] START max_depth=3, min_samples_leaf=4, n_estimators=8.........
[CV 1/5; 118/400] END max_depth=3, min_samples_leaf=4, n_estimators=8;, score=0.796 total time=   0.0s
[CV 2/5; 118/400] START max_depth=3, min_samples_leaf=4, n_estimators=8.........
[CV 2/5; 118/400] END max_depth=3, min_samples_leaf=4, n_estimators=8;, score=0.789 total time=   0.0s
[CV 3/5; 118/400] START max_depth=3, min_samples_leaf=4, n_estimators=8.........
[CV 3/5; 118/400] END max_depth=3, min_samples_leaf=4, n_estimators=8;, score=0.780 total time=   0.0s
[CV 4/5; 118/400] START max_depth=3, min_samples_leaf=4, n_estimators=8.........
[CV 4/5; 118/400] END max_depth=3, min_samples_leaf=4, n_estimators=8;, score=0.799 total time=   0.0s
[CV 5/5; 118/400] START max_depth=3, min_samples_leaf=4, n_estimators=8.........
[CV 5/5; 118/400] END max_depth=3, min_samples_leaf=4, n_estimators=8;, score=0.777 total time=   0.0s
[CV 1/5; 119/400] START max_depth=3, min_samples_leaf=4, n_estimators=9.........
[CV 1/5; 119/400] END max_depth=3, min_samples_leaf=4, n_estimators=9;, score=0.787 total time=   0.0s
[CV 2/5; 119/400] START max_depth=3, min_samples_leaf=4, n_estimators=9.........
[CV 2/5; 119/400] END max_depth=3, min_samples_leaf=4, n_estimators=9;, score=0.791 total time=   0.0s
[CV 3/5; 119/400] START max_depth=3, min_samples_leaf=4, n_estimators=9.........
[CV 3/5; 119/400] END max_depth=3, min_samples_leaf=4, n_estimators=9;, score=0.776 total time=   0.0s
[CV 4/5; 119/400] START max_depth=3, min_samples_leaf=4, n_estimators=9.........
[CV 4/5; 119/400] END max_depth=3, min_samples_leaf=4, n_estimators=9;, score=0.794 total time=   0.0s
[CV 5/5; 119/400] START max_depth=3, min_samples_leaf=4, n_estimators=9.........
[CV 5/5; 119/400] END max_depth=3, min_samples_leaf=4, n_estimators=9;, score=0.771 total time=   0.0s
[CV 1/5; 120/400] START max_depth=3, min_samples_leaf=4, n_estimators=10........
[CV 1/5; 120/400] END max_depth=3, min_samples_leaf=4, n_estimators=10;, score=0.790 total time=   0.0s
[CV 2/5; 120/400] START max_depth=3, min_samples_leaf=4, n_estimators=10........
[CV 2/5; 120/400] END max_depth=3, min_samples_leaf=4, n_estimators=10;, score=0.791 total time=   0.0s
[CV 3/5; 120/400] START max_depth=3, min_samples_leaf=4, n_estimators=10........
[CV 3/5; 120/400] END max_depth=3, min_samples_leaf=4, n_estimators=10;, score=0.780 total time=   0.0s
[CV 4/5; 120/400] START max_depth=3, min_samples_leaf=4, n_estimators=10........
[CV 4/5; 120/400] END max_depth=3, min_samples_leaf=4, n_estimators=10;, score=0.791 total time=   0.0s
[CV 5/5; 120/400] START max_depth=3, min_samples_leaf=4, n_estimators=10........
[CV 5/5; 120/400] END max_depth=3, min_samples_leaf=4, n_estimators=10;, score=0.770 total time=   0.0s
[CV 1/5; 121/400] START max_depth=4, min_samples_leaf=1, n_estimators=1.........
[CV 1/5; 121/400] END max_depth=4, min_samples_leaf=1, n_estimators=1;, score=0.776 total time=   0.0s
[CV 2/5; 121/400] START max_depth=4, min_samples_leaf=1, n_estimators=1.........
[CV 2/5; 121/400] END max_depth=4, min_samples_leaf=1, n_estimators=1;, score=0.791 total time=   0.0s
[CV 3/5; 121/400] START max_depth=4, min_samples_leaf=1, n_estimators=1.........
[CV 3/5; 121/400] END max_depth=4, min_samples_leaf=1, n_estimators=1;, score=0.785 total time=   0.0s
[CV 4/5; 121/400] START max_depth=4, min_samples_leaf=1, n_estimators=1.........
[CV 4/5; 121/400] END max_depth=4, min_samples_leaf=1, n_estimators=1;, score=0.659 total time=   0.0s
[CV 5/5; 121/400] START max_depth=4, min_samples_leaf=1, n_estimators=1.........
[CV 5/5; 121/400] END max_depth=4, min_samples_leaf=1, n_estimators=1;, score=0.760 total time=   0.0s
[CV 1/5; 122/400] START max_depth=4, min_samples_leaf=1, n_estimators=2.........
[CV 1/5; 122/400] END max_depth=4, min_samples_leaf=1, n_estimators=2;, score=0.796 total time=   0.0s
[CV 2/5; 122/400] START max_depth=4, min_samples_leaf=1, n_estimators=2.........
[CV 2/5; 122/400] END max_depth=4, min_samples_leaf=1, n_estimators=2;, score=0.791 total time=   0.0s
[CV 3/5; 122/400] START max_depth=4, min_samples_leaf=1, n_estimators=2.........
[CV 3/5; 122/400] END max_depth=4, min_samples_leaf=1, n_estimators=2;, score=0.797 total time=   0.0s
[CV 4/5; 122/400] START max_depth=4, min_samples_leaf=1, n_estimators=2.........
[CV 4/5; 122/400] END max_depth=4, min_samples_leaf=1, n_estimators=2;, score=0.788 total time=   0.0s
[CV 5/5; 122/400] START max_depth=4, min_samples_leaf=1, n_estimators=2.........
[CV 5/5; 122/400] END max_depth=4, min_samples_leaf=1, n_estimators=2;, score=0.786 total time=   0.0s
[CV 1/5; 123/400] START max_depth=4, min_samples_leaf=1, n_estimators=3.........
[CV 1/5; 123/400] END max_depth=4, min_samples_leaf=1, n_estimators=3;, score=0.792 total time=   0.0s
[CV 2/5; 123/400] START max_depth=4, min_samples_leaf=1, n_estimators=3.........
[CV 2/5; 123/400] END max_depth=4, min_samples_leaf=1, n_estimators=3;, score=0.793 total time=   0.0s
[CV 3/5; 123/400] START max_depth=4, min_samples_leaf=1, n_estimators=3.........
[CV 3/5; 123/400] END max_depth=4, min_samples_leaf=1, n_estimators=3;, score=0.783 total time=   0.0s
[CV 4/5; 123/400] START max_depth=4, min_samples_leaf=1, n_estimators=3.........
[CV 4/5; 123/400] END max_depth=4, min_samples_leaf=1, n_estimators=3;, score=0.793 total time=   0.0s
[CV 5/5; 123/400] START max_depth=4, min_samples_leaf=1, n_estimators=3.........
[CV 5/5; 123/400] END max_depth=4, min_samples_leaf=1, n_estimators=3;, score=0.785 total time=   0.0s
[CV 1/5; 124/400] START max_depth=4, min_samples_leaf=1, n_estimators=4.........
[CV 1/5; 124/400] END max_depth=4, min_samples_leaf=1, n_estimators=4;, score=0.805 total time=   0.0s
[CV 2/5; 124/400] START max_depth=4, min_samples_leaf=1, n_estimators=4.........
[CV 2/5; 124/400] END max_depth=4, min_samples_leaf=1, n_estimators=4;, score=0.793 total time=   0.0s
[CV 3/5; 124/400] START max_depth=4, min_samples_leaf=1, n_estimators=4.........
[CV 3/5; 124/400] END max_depth=4, min_samples_leaf=1, n_estimators=4;, score=0.780 total time=   0.0s
[CV 4/5; 124/400] START max_depth=4, min_samples_leaf=1, n_estimators=4.........
[CV 4/5; 124/400] END max_depth=4, min_samples_leaf=1, n_estimators=4;, score=0.796 total time=   0.0s
[CV 5/5; 124/400] START max_depth=4, min_samples_leaf=1, n_estimators=4.........
[CV 5/5; 124/400] END max_depth=4, min_samples_leaf=1, n_estimators=4;, score=0.786 total time=   0.0s
[CV 1/5; 125/400] START max_depth=4, min_samples_leaf=1, n_estimators=5.........
[CV 1/5; 125/400] END max_depth=4, min_samples_leaf=1, n_estimators=5;, score=0.811 total time=   0.0s
[CV 2/5; 125/400] START max_depth=4, min_samples_leaf=1, n_estimators=5.........
[CV 2/5; 125/400] END max_depth=4, min_samples_leaf=1, n_estimators=5;, score=0.791 total time=   0.0s
[CV 3/5; 125/400] START max_depth=4, min_samples_leaf=1, n_estimators=5.........
[CV 3/5; 125/400] END max_depth=4, min_samples_leaf=1, n_estimators=5;, score=0.785 total time=   0.0s
[CV 4/5; 125/400] START max_depth=4, min_samples_leaf=1, n_estimators=5.........
[CV 4/5; 125/400] END max_depth=4, min_samples_leaf=1, n_estimators=5;, score=0.802 total time=   0.0s
[CV 5/5; 125/400] START max_depth=4, min_samples_leaf=1, n_estimators=5.........
[CV 5/5; 125/400] END max_depth=4, min_samples_leaf=1, n_estimators=5;, score=0.771 total time=   0.0s
[CV 1/5; 126/400] START max_depth=4, min_samples_leaf=1, n_estimators=6.........
[CV 1/5; 126/400] END max_depth=4, min_samples_leaf=1, n_estimators=6;, score=0.802 total time=   0.0s
[CV 2/5; 126/400] START max_depth=4, min_samples_leaf=1, n_estimators=6.........
[CV 2/5; 126/400] END max_depth=4, min_samples_leaf=1, n_estimators=6;, score=0.795 total time=   0.0s
[CV 3/5; 126/400] START max_depth=4, min_samples_leaf=1, n_estimators=6.........
[CV 3/5; 126/400] END max_depth=4, min_samples_leaf=1, n_estimators=6;, score=0.788 total time=   0.0s
[CV 4/5; 126/400] START max_depth=4, min_samples_leaf=1, n_estimators=6.........
[CV 4/5; 126/400] END max_depth=4, min_samples_leaf=1, n_estimators=6;, score=0.799 total time=   0.0s
[CV 5/5; 126/400] START max_depth=4, min_samples_leaf=1, n_estimators=6.........
[CV 5/5; 126/400] END max_depth=4, min_samples_leaf=1, n_estimators=6;, score=0.775 total time=   0.0s
[CV 1/5; 127/400] START max_depth=4, min_samples_leaf=1, n_estimators=7.........
[CV 1/5; 127/400] END max_depth=4, min_samples_leaf=1, n_estimators=7;, score=0.798 total time=   0.0s
[CV 2/5; 127/400] START max_depth=4, min_samples_leaf=1, n_estimators=7.........
[CV 2/5; 127/400] END max_depth=4, min_samples_leaf=1, n_estimators=7;, score=0.796 total time=   0.0s
[CV 3/5; 127/400] START max_depth=4, min_samples_leaf=1, n_estimators=7.........
[CV 3/5; 127/400] END max_depth=4, min_samples_leaf=1, n_estimators=7;, score=0.785 total time=   0.0s
[CV 4/5; 127/400] START max_depth=4, min_samples_leaf=1, n_estimators=7.........
[CV 4/5; 127/400] END max_depth=4, min_samples_leaf=1, n_estimators=7;, score=0.794 total time=   0.0s
[CV 5/5; 127/400] START max_depth=4, min_samples_leaf=1, n_estimators=7.........
[CV 5/5; 127/400] END max_depth=4, min_samples_leaf=1, n_estimators=7;, score=0.783 total time=   0.0s
[CV 1/5; 128/400] START max_depth=4, min_samples_leaf=1, n_estimators=8.........
[CV 1/5; 128/400] END max_depth=4, min_samples_leaf=1, n_estimators=8;, score=0.799 total time=   0.0s
[CV 2/5; 128/400] START max_depth=4, min_samples_leaf=1, n_estimators=8.........
[CV 2/5; 128/400] END max_depth=4, min_samples_leaf=1, n_estimators=8;, score=0.790 total time=   0.0s
[CV 3/5; 128/400] START max_depth=4, min_samples_leaf=1, n_estimators=8.........
[CV 3/5; 128/400] END max_depth=4, min_samples_leaf=1, n_estimators=8;, score=0.787 total time=   0.0s
[CV 4/5; 128/400] START max_depth=4, min_samples_leaf=1, n_estimators=8.........
[CV 4/5; 128/400] END max_depth=4, min_samples_leaf=1, n_estimators=8;, score=0.796 total time=   0.0s
[CV 5/5; 128/400] START max_depth=4, min_samples_leaf=1, n_estimators=8.........
[CV 5/5; 128/400] END max_depth=4, min_samples_leaf=1, n_estimators=8;, score=0.777 total time=   0.0s
[CV 1/5; 129/400] START max_depth=4, min_samples_leaf=1, n_estimators=9.........
[CV 1/5; 129/400] END max_depth=4, min_samples_leaf=1, n_estimators=9;, score=0.794 total time=   0.0s
[CV 2/5; 129/400] START max_depth=4, min_samples_leaf=1, n_estimators=9.........
[CV 2/5; 129/400] END max_depth=4, min_samples_leaf=1, n_estimators=9;, score=0.796 total time=   0.0s
[CV 3/5; 129/400] START max_depth=4, min_samples_leaf=1, n_estimators=9.........
[CV 3/5; 129/400] END max_depth=4, min_samples_leaf=1, n_estimators=9;, score=0.780 total time=   0.0s
[CV 4/5; 129/400] START max_depth=4, min_samples_leaf=1, n_estimators=9.........
[CV 4/5; 129/400] END max_depth=4, min_samples_leaf=1, n_estimators=9;, score=0.799 total time=   0.1s
[CV 5/5; 129/400] START max_depth=4, min_samples_leaf=1, n_estimators=9.........
[CV 5/5; 129/400] END max_depth=4, min_samples_leaf=1, n_estimators=9;, score=0.775 total time=   0.1s
[CV 1/5; 130/400] START max_depth=4, min_samples_leaf=1, n_estimators=10........
[CV 1/5; 130/400] END max_depth=4, min_samples_leaf=1, n_estimators=10;, score=0.804 total time=   0.1s
[CV 2/5; 130/400] START max_depth=4, min_samples_leaf=1, n_estimators=10........
[CV 2/5; 130/400] END max_depth=4, min_samples_leaf=1, n_estimators=10;, score=0.794 total time=   0.1s
[CV 3/5; 130/400] START max_depth=4, min_samples_leaf=1, n_estimators=10........
[CV 3/5; 130/400] END max_depth=4, min_samples_leaf=1, n_estimators=10;, score=0.791 total time=   0.1s
[CV 4/5; 130/400] START max_depth=4, min_samples_leaf=1, n_estimators=10........
[CV 4/5; 130/400] END max_depth=4, min_samples_leaf=1, n_estimators=10;, score=0.798 total time=   0.0s
[CV 5/5; 130/400] START max_depth=4, min_samples_leaf=1, n_estimators=10........
[CV 5/5; 130/400] END max_depth=4, min_samples_leaf=1, n_estimators=10;, score=0.781 total time=   0.1s
[CV 1/5; 131/400] START max_depth=4, min_samples_leaf=2, n_estimators=1.........
[CV 1/5; 131/400] END max_depth=4, min_samples_leaf=2, n_estimators=1;, score=0.795 total time=   0.0s
[CV 2/5; 131/400] START max_depth=4, min_samples_leaf=2, n_estimators=1.........
[CV 2/5; 131/400] END max_depth=4, min_samples_leaf=2, n_estimators=1;, score=0.794 total time=   0.0s
[CV 3/5; 131/400] START max_depth=4, min_samples_leaf=2, n_estimators=1.........
[CV 3/5; 131/400] END max_depth=4, min_samples_leaf=2, n_estimators=1;, score=0.788 total time=   0.0s
[CV 4/5; 131/400] START max_depth=4, min_samples_leaf=2, n_estimators=1.........
[CV 4/5; 131/400] END max_depth=4, min_samples_leaf=2, n_estimators=1;, score=0.801 total time=   0.0s
[CV 5/5; 131/400] START max_depth=4, min_samples_leaf=2, n_estimators=1.........
[CV 5/5; 131/400] END max_depth=4, min_samples_leaf=2, n_estimators=1;, score=0.755 total time=   0.0s
[CV 1/5; 132/400] START max_depth=4, min_samples_leaf=2, n_estimators=2.........
[CV 1/5; 132/400] END max_depth=4, min_samples_leaf=2, n_estimators=2;, score=0.814 total time=   0.0s
[CV 2/5; 132/400] START max_depth=4, min_samples_leaf=2, n_estimators=2.........
[CV 2/5; 132/400] END max_depth=4, min_samples_leaf=2, n_estimators=2;, score=0.793 total time=   0.0s
[CV 3/5; 132/400] START max_depth=4, min_samples_leaf=2, n_estimators=2.........
[CV 3/5; 132/400] END max_depth=4, min_samples_leaf=2, n_estimators=2;, score=0.779 total time=   0.0s
[CV 4/5; 132/400] START max_depth=4, min_samples_leaf=2, n_estimators=2.........
[CV 4/5; 132/400] END max_depth=4, min_samples_leaf=2, n_estimators=2;, score=0.787 total time=   0.0s
[CV 5/5; 132/400] START max_depth=4, min_samples_leaf=2, n_estimators=2.........
[CV 5/5; 132/400] END max_depth=4, min_samples_leaf=2, n_estimators=2;, score=0.790 total time=   0.0s
[CV 1/5; 133/400] START max_depth=4, min_samples_leaf=2, n_estimators=3.........
[CV 1/5; 133/400] END max_depth=4, min_samples_leaf=2, n_estimators=3;, score=0.803 total time=   0.0s
[CV 2/5; 133/400] START max_depth=4, min_samples_leaf=2, n_estimators=3.........
[CV 2/5; 133/400] END max_depth=4, min_samples_leaf=2, n_estimators=3;, score=0.794 total time=   0.0s
[CV 3/5; 133/400] START max_depth=4, min_samples_leaf=2, n_estimators=3.........
[CV 3/5; 133/400] END max_depth=4, min_samples_leaf=2, n_estimators=3;, score=0.783 total time=   0.0s
[CV 4/5; 133/400] START max_depth=4, min_samples_leaf=2, n_estimators=3.........
[CV 4/5; 133/400] END max_depth=4, min_samples_leaf=2, n_estimators=3;, score=0.795 total time=   0.0s
[CV 5/5; 133/400] START max_depth=4, min_samples_leaf=2, n_estimators=3.........
[CV 5/5; 133/400] END max_depth=4, min_samples_leaf=2, n_estimators=3;, score=0.792 total time=   0.0s
[CV 1/5; 134/400] START max_depth=4, min_samples_leaf=2, n_estimators=4.........
[CV 1/5; 134/400] END max_depth=4, min_samples_leaf=2, n_estimators=4;, score=0.799 total time=   0.0s
[CV 2/5; 134/400] START max_depth=4, min_samples_leaf=2, n_estimators=4.........
[CV 2/5; 134/400] END max_depth=4, min_samples_leaf=2, n_estimators=4;, score=0.758 total time=   0.0s
[CV 3/5; 134/400] START max_depth=4, min_samples_leaf=2, n_estimators=4.........
[CV 3/5; 134/400] END max_depth=4, min_samples_leaf=2, n_estimators=4;, score=0.794 total time=   0.0s
[CV 4/5; 134/400] START max_depth=4, min_samples_leaf=2, n_estimators=4.........
[CV 4/5; 134/400] END max_depth=4, min_samples_leaf=2, n_estimators=4;, score=0.793 total time=   0.0s
[CV 5/5; 134/400] START max_depth=4, min_samples_leaf=2, n_estimators=4.........
[CV 5/5; 134/400] END max_depth=4, min_samples_leaf=2, n_estimators=4;, score=0.783 total time=   0.0s
[CV 1/5; 135/400] START max_depth=4, min_samples_leaf=2, n_estimators=5.........
[CV 1/5; 135/400] END max_depth=4, min_samples_leaf=2, n_estimators=5;, score=0.777 total time=   0.0s
[CV 2/5; 135/400] START max_depth=4, min_samples_leaf=2, n_estimators=5.........
[CV 2/5; 135/400] END max_depth=4, min_samples_leaf=2, n_estimators=5;, score=0.798 total time=   0.0s
[CV 3/5; 135/400] START max_depth=4, min_samples_leaf=2, n_estimators=5.........
[CV 3/5; 135/400] END max_depth=4, min_samples_leaf=2, n_estimators=5;, score=0.794 total time=   0.0s
[CV 4/5; 135/400] START max_depth=4, min_samples_leaf=2, n_estimators=5.........
[CV 4/5; 135/400] END max_depth=4, min_samples_leaf=2, n_estimators=5;, score=0.791 total time=   0.0s
[CV 5/5; 135/400] START max_depth=4, min_samples_leaf=2, n_estimators=5.........
[CV 5/5; 135/400] END max_depth=4, min_samples_leaf=2, n_estimators=5;, score=0.777 total time=   0.0s
[CV 1/5; 136/400] START max_depth=4, min_samples_leaf=2, n_estimators=6.........
[CV 1/5; 136/400] END max_depth=4, min_samples_leaf=2, n_estimators=6;, score=0.797 total time=   0.0s
[CV 2/5; 136/400] START max_depth=4, min_samples_leaf=2, n_estimators=6.........
[CV 2/5; 136/400] END max_depth=4, min_samples_leaf=2, n_estimators=6;, score=0.790 total time=   0.0s
[CV 3/5; 136/400] START max_depth=4, min_samples_leaf=2, n_estimators=6.........
[CV 3/5; 136/400] END max_depth=4, min_samples_leaf=2, n_estimators=6;, score=0.784 total time=   0.0s
[CV 4/5; 136/400] START max_depth=4, min_samples_leaf=2, n_estimators=6.........
[CV 4/5; 136/400] END max_depth=4, min_samples_leaf=2, n_estimators=6;, score=0.796 total time=   0.0s
[CV 5/5; 136/400] START max_depth=4, min_samples_leaf=2, n_estimators=6.........
[CV 5/5; 136/400] END max_depth=4, min_samples_leaf=2, n_estimators=6;, score=0.780 total time=   0.0s
[CV 1/5; 137/400] START max_depth=4, min_samples_leaf=2, n_estimators=7.........
[CV 1/5; 137/400] END max_depth=4, min_samples_leaf=2, n_estimators=7;, score=0.799 total time=   0.0s
[CV 2/5; 137/400] START max_depth=4, min_samples_leaf=2, n_estimators=7.........
[CV 2/5; 137/400] END max_depth=4, min_samples_leaf=2, n_estimators=7;, score=0.788 total time=   0.0s
[CV 3/5; 137/400] START max_depth=4, min_samples_leaf=2, n_estimators=7.........
[CV 3/5; 137/400] END max_depth=4, min_samples_leaf=2, n_estimators=7;, score=0.783 total time=   0.0s
[CV 4/5; 137/400] START max_depth=4, min_samples_leaf=2, n_estimators=7.........
[CV 4/5; 137/400] END max_depth=4, min_samples_leaf=2, n_estimators=7;, score=0.795 total time=   0.0s
[CV 5/5; 137/400] START max_depth=4, min_samples_leaf=2, n_estimators=7.........
[CV 5/5; 137/400] END max_depth=4, min_samples_leaf=2, n_estimators=7;, score=0.786 total time=   0.0s
[CV 1/5; 138/400] START max_depth=4, min_samples_leaf=2, n_estimators=8.........
[CV 1/5; 138/400] END max_depth=4, min_samples_leaf=2, n_estimators=8;, score=0.801 total time=   0.0s
[CV 2/5; 138/400] START max_depth=4, min_samples_leaf=2, n_estimators=8.........
[CV 2/5; 138/400] END max_depth=4, min_samples_leaf=2, n_estimators=8;, score=0.792 total time=   0.0s
[CV 3/5; 138/400] START max_depth=4, min_samples_leaf=2, n_estimators=8.........
[CV 3/5; 138/400] END max_depth=4, min_samples_leaf=2, n_estimators=8;, score=0.787 total time=   0.0s
[CV 4/5; 138/400] START max_depth=4, min_samples_leaf=2, n_estimators=8.........
[CV 4/5; 138/400] END max_depth=4, min_samples_leaf=2, n_estimators=8;, score=0.793 total time=   0.0s
[CV 5/5; 138/400] START max_depth=4, min_samples_leaf=2, n_estimators=8.........
[CV 5/5; 138/400] END max_depth=4, min_samples_leaf=2, n_estimators=8;, score=0.774 total time=   0.0s
[CV 1/5; 139/400] START max_depth=4, min_samples_leaf=2, n_estimators=9.........
[CV 1/5; 139/400] END max_depth=4, min_samples_leaf=2, n_estimators=9;, score=0.800 total time=   0.0s
[CV 2/5; 139/400] START max_depth=4, min_samples_leaf=2, n_estimators=9.........
[CV 2/5; 139/400] END max_depth=4, min_samples_leaf=2, n_estimators=9;, score=0.801 total time=   0.0s
[CV 3/5; 139/400] START max_depth=4, min_samples_leaf=2, n_estimators=9.........
[CV 3/5; 139/400] END max_depth=4, min_samples_leaf=2, n_estimators=9;, score=0.786 total time=   0.0s
[CV 4/5; 139/400] START max_depth=4, min_samples_leaf=2, n_estimators=9.........
[CV 4/5; 139/400] END max_depth=4, min_samples_leaf=2, n_estimators=9;, score=0.794 total time=   0.0s
[CV 5/5; 139/400] START max_depth=4, min_samples_leaf=2, n_estimators=9.........
[CV 5/5; 139/400] END max_depth=4, min_samples_leaf=2, n_estimators=9;, score=0.783 total time=   0.0s
[CV 1/5; 140/400] START max_depth=4, min_samples_leaf=2, n_estimators=10........
[CV 1/5; 140/400] END max_depth=4, min_samples_leaf=2, n_estimators=10;, score=0.800 total time=   0.1s
[CV 2/5; 140/400] START max_depth=4, min_samples_leaf=2, n_estimators=10........
[CV 2/5; 140/400] END max_depth=4, min_samples_leaf=2, n_estimators=10;, score=0.790 total time=   0.1s
[CV 3/5; 140/400] START max_depth=4, min_samples_leaf=2, n_estimators=10........
[CV 3/5; 140/400] END max_depth=4, min_samples_leaf=2, n_estimators=10;, score=0.780 total time=   0.0s
[CV 4/5; 140/400] START max_depth=4, min_samples_leaf=2, n_estimators=10........
[CV 4/5; 140/400] END max_depth=4, min_samples_leaf=2, n_estimators=10;, score=0.791 total time=   0.1s
[CV 5/5; 140/400] START max_depth=4, min_samples_leaf=2, n_estimators=10........
[CV 5/5; 140/400] END max_depth=4, min_samples_leaf=2, n_estimators=10;, score=0.783 total time=   0.1s
[CV 1/5; 141/400] START max_depth=4, min_samples_leaf=3, n_estimators=1.........
[CV 1/5; 141/400] END max_depth=4, min_samples_leaf=3, n_estimators=1;, score=0.763 total time=   0.0s
[CV 2/5; 141/400] START max_depth=4, min_samples_leaf=3, n_estimators=1.........
[CV 2/5; 141/400] END max_depth=4, min_samples_leaf=3, n_estimators=1;, score=0.738 total time=   0.0s
[CV 3/5; 141/400] START max_depth=4, min_samples_leaf=3, n_estimators=1.........
[CV 3/5; 141/400] END max_depth=4, min_samples_leaf=3, n_estimators=1;, score=0.641 total time=   0.0s
[CV 4/5; 141/400] START max_depth=4, min_samples_leaf=3, n_estimators=1.........
[CV 4/5; 141/400] END max_depth=4, min_samples_leaf=3, n_estimators=1;, score=0.791 total time=   0.0s
[CV 5/5; 141/400] START max_depth=4, min_samples_leaf=3, n_estimators=1.........
[CV 5/5; 141/400] END max_depth=4, min_samples_leaf=3, n_estimators=1;, score=0.769 total time=   0.0s
[CV 1/5; 142/400] START max_depth=4, min_samples_leaf=3, n_estimators=2.........
[CV 1/5; 142/400] END max_depth=4, min_samples_leaf=3, n_estimators=2;, score=0.796 total time=   0.0s
[CV 2/5; 142/400] START max_depth=4, min_samples_leaf=3, n_estimators=2.........
[CV 2/5; 142/400] END max_depth=4, min_samples_leaf=3, n_estimators=2;, score=0.778 total time=   0.0s
[CV 3/5; 142/400] START max_depth=4, min_samples_leaf=3, n_estimators=2.........
[CV 3/5; 142/400] END max_depth=4, min_samples_leaf=3, n_estimators=2;, score=0.796 total time=   0.0s
[CV 4/5; 142/400] START max_depth=4, min_samples_leaf=3, n_estimators=2.........
[CV 4/5; 142/400] END max_depth=4, min_samples_leaf=3, n_estimators=2;, score=0.789 total time=   0.0s
[CV 5/5; 142/400] START max_depth=4, min_samples_leaf=3, n_estimators=2.........
[CV 5/5; 142/400] END max_depth=4, min_samples_leaf=3, n_estimators=2;, score=0.780 total time=   0.0s
[CV 1/5; 143/400] START max_depth=4, min_samples_leaf=3, n_estimators=3.........
[CV 1/5; 143/400] END max_depth=4, min_samples_leaf=3, n_estimators=3;, score=0.804 total time=   0.0s
[CV 2/5; 143/400] START max_depth=4, min_samples_leaf=3, n_estimators=3.........
[CV 2/5; 143/400] END max_depth=4, min_samples_leaf=3, n_estimators=3;, score=0.802 total time=   0.0s
[CV 3/5; 143/400] START max_depth=4, min_samples_leaf=3, n_estimators=3.........
[CV 3/5; 143/400] END max_depth=4, min_samples_leaf=3, n_estimators=3;, score=0.780 total time=   0.0s
[CV 4/5; 143/400] START max_depth=4, min_samples_leaf=3, n_estimators=3.........
[CV 4/5; 143/400] END max_depth=4, min_samples_leaf=3, n_estimators=3;, score=0.785 total time=   0.0s
[CV 5/5; 143/400] START max_depth=4, min_samples_leaf=3, n_estimators=3.........
[CV 5/5; 143/400] END max_depth=4, min_samples_leaf=3, n_estimators=3;, score=0.777 total time=   0.0s
[CV 1/5; 144/400] START max_depth=4, min_samples_leaf=3, n_estimators=4.........
[CV 1/5; 144/400] END max_depth=4, min_samples_leaf=3, n_estimators=4;, score=0.797 total time=   0.0s
[CV 2/5; 144/400] START max_depth=4, min_samples_leaf=3, n_estimators=4.........
[CV 2/5; 144/400] END max_depth=4, min_samples_leaf=3, n_estimators=4;, score=0.789 total time=   0.0s
[CV 3/5; 144/400] START max_depth=4, min_samples_leaf=3, n_estimators=4.........
[CV 3/5; 144/400] END max_depth=4, min_samples_leaf=3, n_estimators=4;, score=0.782 total time=   0.0s
[CV 4/5; 144/400] START max_depth=4, min_samples_leaf=3, n_estimators=4.........
[CV 4/5; 144/400] END max_depth=4, min_samples_leaf=3, n_estimators=4;, score=0.796 total time=   0.0s
[CV 5/5; 144/400] START max_depth=4, min_samples_leaf=3, n_estimators=4.........
[CV 5/5; 144/400] END max_depth=4, min_samples_leaf=3, n_estimators=4;, score=0.778 total time=   0.0s
[CV 1/5; 145/400] START max_depth=4, min_samples_leaf=3, n_estimators=5.........
[CV 1/5; 145/400] END max_depth=4, min_samples_leaf=3, n_estimators=5;, score=0.800 total time=   0.0s
[CV 2/5; 145/400] START max_depth=4, min_samples_leaf=3, n_estimators=5.........
[CV 2/5; 145/400] END max_depth=4, min_samples_leaf=3, n_estimators=5;, score=0.801 total time=   0.0s
[CV 3/5; 145/400] START max_depth=4, min_samples_leaf=3, n_estimators=5.........
[CV 3/5; 145/400] END max_depth=4, min_samples_leaf=3, n_estimators=5;, score=0.799 total time=   0.0s
[CV 4/5; 145/400] START max_depth=4, min_samples_leaf=3, n_estimators=5.........
[CV 4/5; 145/400] END max_depth=4, min_samples_leaf=3, n_estimators=5;, score=0.796 total time=   0.0s
[CV 5/5; 145/400] START max_depth=4, min_samples_leaf=3, n_estimators=5.........
[CV 5/5; 145/400] END max_depth=4, min_samples_leaf=3, n_estimators=5;, score=0.791 total time=   0.0s
[CV 1/5; 146/400] START max_depth=4, min_samples_leaf=3, n_estimators=6.........
[CV 1/5; 146/400] END max_depth=4, min_samples_leaf=3, n_estimators=6;, score=0.801 total time=   0.0s
[CV 2/5; 146/400] START max_depth=4, min_samples_leaf=3, n_estimators=6.........
[CV 2/5; 146/400] END max_depth=4, min_samples_leaf=3, n_estimators=6;, score=0.794 total time=   0.0s
[CV 3/5; 146/400] START max_depth=4, min_samples_leaf=3, n_estimators=6.........
[CV 3/5; 146/400] END max_depth=4, min_samples_leaf=3, n_estimators=6;, score=0.779 total time=   0.0s
[CV 4/5; 146/400] START max_depth=4, min_samples_leaf=3, n_estimators=6.........
[CV 4/5; 146/400] END max_depth=4, min_samples_leaf=3, n_estimators=6;, score=0.794 total time=   0.0s
[CV 5/5; 146/400] START max_depth=4, min_samples_leaf=3, n_estimators=6.........
[CV 5/5; 146/400] END max_depth=4, min_samples_leaf=3, n_estimators=6;, score=0.777 total time=   0.0s
[CV 1/5; 147/400] START max_depth=4, min_samples_leaf=3, n_estimators=7.........
[CV 1/5; 147/400] END max_depth=4, min_samples_leaf=3, n_estimators=7;, score=0.801 total time=   0.0s
[CV 2/5; 147/400] START max_depth=4, min_samples_leaf=3, n_estimators=7.........
[CV 2/5; 147/400] END max_depth=4, min_samples_leaf=3, n_estimators=7;, score=0.792 total time=   0.0s
[CV 3/5; 147/400] START max_depth=4, min_samples_leaf=3, n_estimators=7.........
[CV 3/5; 147/400] END max_depth=4, min_samples_leaf=3, n_estimators=7;, score=0.789 total time=   0.0s
[CV 4/5; 147/400] START max_depth=4, min_samples_leaf=3, n_estimators=7.........
[CV 4/5; 147/400] END max_depth=4, min_samples_leaf=3, n_estimators=7;, score=0.791 total time=   0.0s
[CV 5/5; 147/400] START max_depth=4, min_samples_leaf=3, n_estimators=7.........
[CV 5/5; 147/400] END max_depth=4, min_samples_leaf=3, n_estimators=7;, score=0.773 total time=   0.0s
[CV 1/5; 148/400] START max_depth=4, min_samples_leaf=3, n_estimators=8.........
[CV 1/5; 148/400] END max_depth=4, min_samples_leaf=3, n_estimators=8;, score=0.804 total time=   0.0s
[CV 2/5; 148/400] START max_depth=4, min_samples_leaf=3, n_estimators=8.........
[CV 2/5; 148/400] END max_depth=4, min_samples_leaf=3, n_estimators=8;, score=0.790 total time=   0.0s
[CV 3/5; 148/400] START max_depth=4, min_samples_leaf=3, n_estimators=8.........
[CV 3/5; 148/400] END max_depth=4, min_samples_leaf=3, n_estimators=8;, score=0.782 total time=   0.0s
[CV 4/5; 148/400] START max_depth=4, min_samples_leaf=3, n_estimators=8.........
[CV 4/5; 148/400] END max_depth=4, min_samples_leaf=3, n_estimators=8;, score=0.796 total time=   0.0s
[CV 5/5; 148/400] START max_depth=4, min_samples_leaf=3, n_estimators=8.........
[CV 5/5; 148/400] END max_depth=4, min_samples_leaf=3, n_estimators=8;, score=0.777 total time=   0.1s
[CV 1/5; 149/400] START max_depth=4, min_samples_leaf=3, n_estimators=9.........
[CV 1/5; 149/400] END max_depth=4, min_samples_leaf=3, n_estimators=9;, score=0.796 total time=   0.0s
[CV 2/5; 149/400] START max_depth=4, min_samples_leaf=3, n_estimators=9.........
[CV 2/5; 149/400] END max_depth=4, min_samples_leaf=3, n_estimators=9;, score=0.798 total time=   0.1s
[CV 3/5; 149/400] START max_depth=4, min_samples_leaf=3, n_estimators=9.........
[CV 3/5; 149/400] END max_depth=4, min_samples_leaf=3, n_estimators=9;, score=0.792 total time=   0.0s
[CV 4/5; 149/400] START max_depth=4, min_samples_leaf=3, n_estimators=9.........
[CV 4/5; 149/400] END max_depth=4, min_samples_leaf=3, n_estimators=9;, score=0.796 total time=   0.0s
[CV 5/5; 149/400] START max_depth=4, min_samples_leaf=3, n_estimators=9.........
[CV 5/5; 149/400] END max_depth=4, min_samples_leaf=3, n_estimators=9;, score=0.774 total time=   0.1s
[CV 1/5; 150/400] START max_depth=4, min_samples_leaf=3, n_estimators=10........
[CV 1/5; 150/400] END max_depth=4, min_samples_leaf=3, n_estimators=10;, score=0.797 total time=   0.0s
[CV 2/5; 150/400] START max_depth=4, min_samples_leaf=3, n_estimators=10........
[CV 2/5; 150/400] END max_depth=4, min_samples_leaf=3, n_estimators=10;, score=0.792 total time=   0.0s
[CV 3/5; 150/400] START max_depth=4, min_samples_leaf=3, n_estimators=10........
[CV 3/5; 150/400] END max_depth=4, min_samples_leaf=3, n_estimators=10;, score=0.790 total time=   0.1s
[CV 4/5; 150/400] START max_depth=4, min_samples_leaf=3, n_estimators=10........
[CV 4/5; 150/400] END max_depth=4, min_samples_leaf=3, n_estimators=10;, score=0.796 total time=   0.1s
[CV 5/5; 150/400] START max_depth=4, min_samples_leaf=3, n_estimators=10........
[CV 5/5; 150/400] END max_depth=4, min_samples_leaf=3, n_estimators=10;, score=0.776 total time=   0.0s
[CV 1/5; 151/400] START max_depth=4, min_samples_leaf=4, n_estimators=1.........
[CV 1/5; 151/400] END max_depth=4, min_samples_leaf=4, n_estimators=1;, score=0.769 total time=   0.0s
[CV 2/5; 151/400] START max_depth=4, min_samples_leaf=4, n_estimators=1.........
[CV 2/5; 151/400] END max_depth=4, min_samples_leaf=4, n_estimators=1;, score=0.682 total time=   0.0s
[CV 3/5; 151/400] START max_depth=4, min_samples_leaf=4, n_estimators=1.........
[CV 3/5; 151/400] END max_depth=4, min_samples_leaf=4, n_estimators=1;, score=0.680 total time=   0.0s
[CV 4/5; 151/400] START max_depth=4, min_samples_leaf=4, n_estimators=1.........
[CV 4/5; 151/400] END max_depth=4, min_samples_leaf=4, n_estimators=1;, score=0.771 total time=   0.0s
[CV 5/5; 151/400] START max_depth=4, min_samples_leaf=4, n_estimators=1.........
[CV 5/5; 151/400] END max_depth=4, min_samples_leaf=4, n_estimators=1;, score=0.717 total time=   0.0s
[CV 1/5; 152/400] START max_depth=4, min_samples_leaf=4, n_estimators=2.........
[CV 1/5; 152/400] END max_depth=4, min_samples_leaf=4, n_estimators=2;, score=0.797 total time=   0.0s
[CV 2/5; 152/400] START max_depth=4, min_samples_leaf=4, n_estimators=2.........
[CV 2/5; 152/400] END max_depth=4, min_samples_leaf=4, n_estimators=2;, score=0.802 total time=   0.0s
[CV 3/5; 152/400] START max_depth=4, min_samples_leaf=4, n_estimators=2.........
[CV 3/5; 152/400] END max_depth=4, min_samples_leaf=4, n_estimators=2;, score=0.779 total time=   0.0s
[CV 4/5; 152/400] START max_depth=4, min_samples_leaf=4, n_estimators=2.........
[CV 4/5; 152/400] END max_depth=4, min_samples_leaf=4, n_estimators=2;, score=0.798 total time=   0.0s
[CV 5/5; 152/400] START max_depth=4, min_samples_leaf=4, n_estimators=2.........
[CV 5/5; 152/400] END max_depth=4, min_samples_leaf=4, n_estimators=2;, score=0.766 total time=   0.0s
[CV 1/5; 153/400] START max_depth=4, min_samples_leaf=4, n_estimators=3.........
[CV 1/5; 153/400] END max_depth=4, min_samples_leaf=4, n_estimators=3;, score=0.809 total time=   0.0s
[CV 2/5; 153/400] START max_depth=4, min_samples_leaf=4, n_estimators=3.........
[CV 2/5; 153/400] END max_depth=4, min_samples_leaf=4, n_estimators=3;, score=0.801 total time=   0.0s
[CV 3/5; 153/400] START max_depth=4, min_samples_leaf=4, n_estimators=3.........
[CV 3/5; 153/400] END max_depth=4, min_samples_leaf=4, n_estimators=3;, score=0.774 total time=   0.0s
[CV 4/5; 153/400] START max_depth=4, min_samples_leaf=4, n_estimators=3.........
[CV 4/5; 153/400] END max_depth=4, min_samples_leaf=4, n_estimators=3;, score=0.793 total time=   0.0s
[CV 5/5; 153/400] START max_depth=4, min_samples_leaf=4, n_estimators=3.........
[CV 5/5; 153/400] END max_depth=4, min_samples_leaf=4, n_estimators=3;, score=0.774 total time=   0.0s
[CV 1/5; 154/400] START max_depth=4, min_samples_leaf=4, n_estimators=4.........
[CV 1/5; 154/400] END max_depth=4, min_samples_leaf=4, n_estimators=4;, score=0.795 total time=   0.0s
[CV 2/5; 154/400] START max_depth=4, min_samples_leaf=4, n_estimators=4.........
[CV 2/5; 154/400] END max_depth=4, min_samples_leaf=4, n_estimators=4;, score=0.796 total time=   0.0s
[CV 3/5; 154/400] START max_depth=4, min_samples_leaf=4, n_estimators=4.........
[CV 3/5; 154/400] END max_depth=4, min_samples_leaf=4, n_estimators=4;, score=0.783 total time=   0.0s
[CV 4/5; 154/400] START max_depth=4, min_samples_leaf=4, n_estimators=4.........
[CV 4/5; 154/400] END max_depth=4, min_samples_leaf=4, n_estimators=4;, score=0.807 total time=   0.0s
[CV 5/5; 154/400] START max_depth=4, min_samples_leaf=4, n_estimators=4.........
[CV 5/5; 154/400] END max_depth=4, min_samples_leaf=4, n_estimators=4;, score=0.785 total time=   0.0s
[CV 1/5; 155/400] START max_depth=4, min_samples_leaf=4, n_estimators=5.........
[CV 1/5; 155/400] END max_depth=4, min_samples_leaf=4, n_estimators=5;, score=0.798 total time=   0.0s
[CV 2/5; 155/400] START max_depth=4, min_samples_leaf=4, n_estimators=5.........
[CV 2/5; 155/400] END max_depth=4, min_samples_leaf=4, n_estimators=5;, score=0.794 total time=   0.0s
[CV 3/5; 155/400] START max_depth=4, min_samples_leaf=4, n_estimators=5.........
[CV 3/5; 155/400] END max_depth=4, min_samples_leaf=4, n_estimators=5;, score=0.788 total time=   0.0s
[CV 4/5; 155/400] START max_depth=4, min_samples_leaf=4, n_estimators=5.........
[CV 4/5; 155/400] END max_depth=4, min_samples_leaf=4, n_estimators=5;, score=0.798 total time=   0.0s
[CV 5/5; 155/400] START max_depth=4, min_samples_leaf=4, n_estimators=5.........
[CV 5/5; 155/400] END max_depth=4, min_samples_leaf=4, n_estimators=5;, score=0.781 total time=   0.0s
[CV 1/5; 156/400] START max_depth=4, min_samples_leaf=4, n_estimators=6.........
[CV 1/5; 156/400] END max_depth=4, min_samples_leaf=4, n_estimators=6;, score=0.796 total time=   0.0s
[CV 2/5; 156/400] START max_depth=4, min_samples_leaf=4, n_estimators=6.........
[CV 2/5; 156/400] END max_depth=4, min_samples_leaf=4, n_estimators=6;, score=0.796 total time=   0.1s
[CV 3/5; 156/400] START max_depth=4, min_samples_leaf=4, n_estimators=6.........
[CV 3/5; 156/400] END max_depth=4, min_samples_leaf=4, n_estimators=6;, score=0.794 total time=   0.0s
[CV 4/5; 156/400] START max_depth=4, min_samples_leaf=4, n_estimators=6.........
[CV 4/5; 156/400] END max_depth=4, min_samples_leaf=4, n_estimators=6;, score=0.791 total time=   0.0s
[CV 5/5; 156/400] START max_depth=4, min_samples_leaf=4, n_estimators=6.........
[CV 5/5; 156/400] END max_depth=4, min_samples_leaf=4, n_estimators=6;, score=0.783 total time=   0.0s
[CV 1/5; 157/400] START max_depth=4, min_samples_leaf=4, n_estimators=7.........
[CV 1/5; 157/400] END max_depth=4, min_samples_leaf=4, n_estimators=7;, score=0.797 total time=   0.0s
[CV 2/5; 157/400] START max_depth=4, min_samples_leaf=4, n_estimators=7.........
[CV 2/5; 157/400] END max_depth=4, min_samples_leaf=4, n_estimators=7;, score=0.794 total time=   0.0s
[CV 3/5; 157/400] START max_depth=4, min_samples_leaf=4, n_estimators=7.........
[CV 3/5; 157/400] END max_depth=4, min_samples_leaf=4, n_estimators=7;, score=0.785 total time=   0.1s
[CV 4/5; 157/400] START max_depth=4, min_samples_leaf=4, n_estimators=7.........
[CV 4/5; 157/400] END max_depth=4, min_samples_leaf=4, n_estimators=7;, score=0.803 total time=   0.0s
[CV 5/5; 157/400] START max_depth=4, min_samples_leaf=4, n_estimators=7.........
[CV 5/5; 157/400] END max_depth=4, min_samples_leaf=4, n_estimators=7;, score=0.782 total time=   0.0s
[CV 1/5; 158/400] START max_depth=4, min_samples_leaf=4, n_estimators=8.........
[CV 1/5; 158/400] END max_depth=4, min_samples_leaf=4, n_estimators=8;, score=0.798 total time=   0.0s
[CV 2/5; 158/400] START max_depth=4, min_samples_leaf=4, n_estimators=8.........
[CV 2/5; 158/400] END max_depth=4, min_samples_leaf=4, n_estimators=8;, score=0.791 total time=   0.0s
[CV 3/5; 158/400] START max_depth=4, min_samples_leaf=4, n_estimators=8.........
[CV 3/5; 158/400] END max_depth=4, min_samples_leaf=4, n_estimators=8;, score=0.781 total time=   0.0s
[CV 4/5; 158/400] START max_depth=4, min_samples_leaf=4, n_estimators=8.........
[CV 4/5; 158/400] END max_depth=4, min_samples_leaf=4, n_estimators=8;, score=0.796 total time=   0.0s
[CV 5/5; 158/400] START max_depth=4, min_samples_leaf=4, n_estimators=8.........
[CV 5/5; 158/400] END max_depth=4, min_samples_leaf=4, n_estimators=8;, score=0.780 total time=   0.0s
[CV 1/5; 159/400] START max_depth=4, min_samples_leaf=4, n_estimators=9.........
[CV 1/5; 159/400] END max_depth=4, min_samples_leaf=4, n_estimators=9;, score=0.798 total time=   0.0s
[CV 2/5; 159/400] START max_depth=4, min_samples_leaf=4, n_estimators=9.........
[CV 2/5; 159/400] END max_depth=4, min_samples_leaf=4, n_estimators=9;, score=0.795 total time=   0.0s
[CV 3/5; 159/400] START max_depth=4, min_samples_leaf=4, n_estimators=9.........
[CV 3/5; 159/400] END max_depth=4, min_samples_leaf=4, n_estimators=9;, score=0.783 total time=   0.0s
[CV 4/5; 159/400] START max_depth=4, min_samples_leaf=4, n_estimators=9.........
[CV 4/5; 159/400] END max_depth=4, min_samples_leaf=4, n_estimators=9;, score=0.792 total time=   0.0s
[CV 5/5; 159/400] START max_depth=4, min_samples_leaf=4, n_estimators=9.........
[CV 5/5; 159/400] END max_depth=4, min_samples_leaf=4, n_estimators=9;, score=0.776 total time=   0.0s
[CV 1/5; 160/400] START max_depth=4, min_samples_leaf=4, n_estimators=10........
[CV 1/5; 160/400] END max_depth=4, min_samples_leaf=4, n_estimators=10;, score=0.796 total time=   0.0s
[CV 2/5; 160/400] START max_depth=4, min_samples_leaf=4, n_estimators=10........
[CV 2/5; 160/400] END max_depth=4, min_samples_leaf=4, n_estimators=10;, score=0.792 total time=   0.1s
[CV 3/5; 160/400] START max_depth=4, min_samples_leaf=4, n_estimators=10........
[CV 3/5; 160/400] END max_depth=4, min_samples_leaf=4, n_estimators=10;, score=0.794 total time=   0.1s
[CV 4/5; 160/400] START max_depth=4, min_samples_leaf=4, n_estimators=10........
[CV 4/5; 160/400] END max_depth=4, min_samples_leaf=4, n_estimators=10;, score=0.794 total time=   0.0s
[CV 5/5; 160/400] START max_depth=4, min_samples_leaf=4, n_estimators=10........
[CV 5/5; 160/400] END max_depth=4, min_samples_leaf=4, n_estimators=10;, score=0.781 total time=   0.1s
[CV 1/5; 161/400] START max_depth=5, min_samples_leaf=1, n_estimators=1.........
[CV 1/5; 161/400] END max_depth=5, min_samples_leaf=1, n_estimators=1;, score=0.643 total time=   0.0s
[CV 2/5; 161/400] START max_depth=5, min_samples_leaf=1, n_estimators=1.........
[CV 2/5; 161/400] END max_depth=5, min_samples_leaf=1, n_estimators=1;, score=0.788 total time=   0.0s
[CV 3/5; 161/400] START max_depth=5, min_samples_leaf=1, n_estimators=1.........
[CV 3/5; 161/400] END max_depth=5, min_samples_leaf=1, n_estimators=1;, score=0.807 total time=   0.0s
[CV 4/5; 161/400] START max_depth=5, min_samples_leaf=1, n_estimators=1.........
[CV 4/5; 161/400] END max_depth=5, min_samples_leaf=1, n_estimators=1;, score=0.793 total time=   0.0s
[CV 5/5; 161/400] START max_depth=5, min_samples_leaf=1, n_estimators=1.........
[CV 5/5; 161/400] END max_depth=5, min_samples_leaf=1, n_estimators=1;, score=0.763 total time=   0.0s
[CV 1/5; 162/400] START max_depth=5, min_samples_leaf=1, n_estimators=2.........
[CV 1/5; 162/400] END max_depth=5, min_samples_leaf=1, n_estimators=2;, score=0.744 total time=   0.0s
[CV 2/5; 162/400] START max_depth=5, min_samples_leaf=1, n_estimators=2.........
[CV 2/5; 162/400] END max_depth=5, min_samples_leaf=1, n_estimators=2;, score=0.794 total time=   0.0s
[CV 3/5; 162/400] START max_depth=5, min_samples_leaf=1, n_estimators=2.........
[CV 3/5; 162/400] END max_depth=5, min_samples_leaf=1, n_estimators=2;, score=0.783 total time=   0.0s
[CV 4/5; 162/400] START max_depth=5, min_samples_leaf=1, n_estimators=2.........
[CV 4/5; 162/400] END max_depth=5, min_samples_leaf=1, n_estimators=2;, score=0.773 total time=   0.0s
[CV 5/5; 162/400] START max_depth=5, min_samples_leaf=1, n_estimators=2.........
[CV 5/5; 162/400] END max_depth=5, min_samples_leaf=1, n_estimators=2;, score=0.790 total time=   0.0s
[CV 1/5; 163/400] START max_depth=5, min_samples_leaf=1, n_estimators=3.........
[CV 1/5; 163/400] END max_depth=5, min_samples_leaf=1, n_estimators=3;, score=0.802 total time=   0.0s
[CV 2/5; 163/400] START max_depth=5, min_samples_leaf=1, n_estimators=3.........
[CV 2/5; 163/400] END max_depth=5, min_samples_leaf=1, n_estimators=3;, score=0.802 total time=   0.0s
[CV 3/5; 163/400] START max_depth=5, min_samples_leaf=1, n_estimators=3.........
[CV 3/5; 163/400] END max_depth=5, min_samples_leaf=1, n_estimators=3;, score=0.788 total time=   0.0s
[CV 4/5; 163/400] START max_depth=5, min_samples_leaf=1, n_estimators=3.........
[CV 4/5; 163/400] END max_depth=5, min_samples_leaf=1, n_estimators=3;, score=0.801 total time=   0.0s
[CV 5/5; 163/400] START max_depth=5, min_samples_leaf=1, n_estimators=3.........
[CV 5/5; 163/400] END max_depth=5, min_samples_leaf=1, n_estimators=3;, score=0.784 total time=   0.0s
[CV 1/5; 164/400] START max_depth=5, min_samples_leaf=1, n_estimators=4.........
[CV 1/5; 164/400] END max_depth=5, min_samples_leaf=1, n_estimators=4;, score=0.785 total time=   0.0s
[CV 2/5; 164/400] START max_depth=5, min_samples_leaf=1, n_estimators=4.........
[CV 2/5; 164/400] END max_depth=5, min_samples_leaf=1, n_estimators=4;, score=0.807 total time=   0.0s
[CV 3/5; 164/400] START max_depth=5, min_samples_leaf=1, n_estimators=4.........
[CV 3/5; 164/400] END max_depth=5, min_samples_leaf=1, n_estimators=4;, score=0.801 total time=   0.0s
[CV 4/5; 164/400] START max_depth=5, min_samples_leaf=1, n_estimators=4.........
[CV 4/5; 164/400] END max_depth=5, min_samples_leaf=1, n_estimators=4;, score=0.808 total time=   0.0s
[CV 5/5; 164/400] START max_depth=5, min_samples_leaf=1, n_estimators=4.........
[CV 5/5; 164/400] END max_depth=5, min_samples_leaf=1, n_estimators=4;, score=0.774 total time=   0.0s
[CV 1/5; 165/400] START max_depth=5, min_samples_leaf=1, n_estimators=5.........
[CV 1/5; 165/400] END max_depth=5, min_samples_leaf=1, n_estimators=5;, score=0.814 total time=   0.0s
[CV 2/5; 165/400] START max_depth=5, min_samples_leaf=1, n_estimators=5.........
[CV 2/5; 165/400] END max_depth=5, min_samples_leaf=1, n_estimators=5;, score=0.809 total time=   0.0s
[CV 3/5; 165/400] START max_depth=5, min_samples_leaf=1, n_estimators=5.........
[CV 3/5; 165/400] END max_depth=5, min_samples_leaf=1, n_estimators=5;, score=0.792 total time=   0.0s
[CV 4/5; 165/400] START max_depth=5, min_samples_leaf=1, n_estimators=5.........
[CV 4/5; 165/400] END max_depth=5, min_samples_leaf=1, n_estimators=5;, score=0.807 total time=   0.0s
[CV 5/5; 165/400] START max_depth=5, min_samples_leaf=1, n_estimators=5.........
[CV 5/5; 165/400] END max_depth=5, min_samples_leaf=1, n_estimators=5;, score=0.808 total time=   0.0s
[CV 1/5; 166/400] START max_depth=5, min_samples_leaf=1, n_estimators=6.........
[CV 1/5; 166/400] END max_depth=5, min_samples_leaf=1, n_estimators=6;, score=0.805 total time=   0.0s
[CV 2/5; 166/400] START max_depth=5, min_samples_leaf=1, n_estimators=6.........
[CV 2/5; 166/400] END max_depth=5, min_samples_leaf=1, n_estimators=6;, score=0.803 total time=   0.0s
[CV 3/5; 166/400] START max_depth=5, min_samples_leaf=1, n_estimators=6.........
[CV 3/5; 166/400] END max_depth=5, min_samples_leaf=1, n_estimators=6;, score=0.801 total time=   0.0s
[CV 4/5; 166/400] START max_depth=5, min_samples_leaf=1, n_estimators=6.........
[CV 4/5; 166/400] END max_depth=5, min_samples_leaf=1, n_estimators=6;, score=0.801 total time=   0.0s
[CV 5/5; 166/400] START max_depth=5, min_samples_leaf=1, n_estimators=6.........
[CV 5/5; 166/400] END max_depth=5, min_samples_leaf=1, n_estimators=6;, score=0.794 total time=   0.0s
[CV 1/5; 167/400] START max_depth=5, min_samples_leaf=1, n_estimators=7.........
[CV 1/5; 167/400] END max_depth=5, min_samples_leaf=1, n_estimators=7;, score=0.810 total time=   0.0s
[CV 2/5; 167/400] START max_depth=5, min_samples_leaf=1, n_estimators=7.........
[CV 2/5; 167/400] END max_depth=5, min_samples_leaf=1, n_estimators=7;, score=0.808 total time=   0.0s
[CV 3/5; 167/400] START max_depth=5, min_samples_leaf=1, n_estimators=7.........
[CV 3/5; 167/400] END max_depth=5, min_samples_leaf=1, n_estimators=7;, score=0.792 total time=   0.0s
[CV 4/5; 167/400] START max_depth=5, min_samples_leaf=1, n_estimators=7.........
[CV 4/5; 167/400] END max_depth=5, min_samples_leaf=1, n_estimators=7;, score=0.805 total time=   0.0s
[CV 5/5; 167/400] START max_depth=5, min_samples_leaf=1, n_estimators=7.........
[CV 5/5; 167/400] END max_depth=5, min_samples_leaf=1, n_estimators=7;, score=0.789 total time=   0.0s
[CV 1/5; 168/400] START max_depth=5, min_samples_leaf=1, n_estimators=8.........
[CV 1/5; 168/400] END max_depth=5, min_samples_leaf=1, n_estimators=8;, score=0.813 total time=   0.0s
[CV 2/5; 168/400] START max_depth=5, min_samples_leaf=1, n_estimators=8.........
[CV 2/5; 168/400] END max_depth=5, min_samples_leaf=1, n_estimators=8;, score=0.801 total time=   0.1s
[CV 3/5; 168/400] START max_depth=5, min_samples_leaf=1, n_estimators=8.........
[CV 3/5; 168/400] END max_depth=5, min_samples_leaf=1, n_estimators=8;, score=0.807 total time=   0.0s
[CV 4/5; 168/400] START max_depth=5, min_samples_leaf=1, n_estimators=8.........
[CV 4/5; 168/400] END max_depth=5, min_samples_leaf=1, n_estimators=8;, score=0.796 total time=   0.0s
[CV 5/5; 168/400] START max_depth=5, min_samples_leaf=1, n_estimators=8.........
[CV 5/5; 168/400] END max_depth=5, min_samples_leaf=1, n_estimators=8;, score=0.794 total time=   0.0s
[CV 1/5; 169/400] START max_depth=5, min_samples_leaf=1, n_estimators=9.........
[CV 1/5; 169/400] END max_depth=5, min_samples_leaf=1, n_estimators=9;, score=0.808 total time=   0.1s
[CV 2/5; 169/400] START max_depth=5, min_samples_leaf=1, n_estimators=9.........
[CV 2/5; 169/400] END max_depth=5, min_samples_leaf=1, n_estimators=9;, score=0.799 total time=   0.0s
[CV 3/5; 169/400] START max_depth=5, min_samples_leaf=1, n_estimators=9.........
[CV 3/5; 169/400] END max_depth=5, min_samples_leaf=1, n_estimators=9;, score=0.797 total time=   0.0s
[CV 4/5; 169/400] START max_depth=5, min_samples_leaf=1, n_estimators=9.........
[CV 4/5; 169/400] END max_depth=5, min_samples_leaf=1, n_estimators=9;, score=0.808 total time=   0.0s
[CV 5/5; 169/400] START max_depth=5, min_samples_leaf=1, n_estimators=9.........
[CV 5/5; 169/400] END max_depth=5, min_samples_leaf=1, n_estimators=9;, score=0.780 total time=   0.0s
[CV 1/5; 170/400] START max_depth=5, min_samples_leaf=1, n_estimators=10........
[CV 1/5; 170/400] END max_depth=5, min_samples_leaf=1, n_estimators=10;, score=0.807 total time=   0.1s
[CV 2/5; 170/400] START max_depth=5, min_samples_leaf=1, n_estimators=10........
[CV 2/5; 170/400] END max_depth=5, min_samples_leaf=1, n_estimators=10;, score=0.808 total time=   0.1s
[CV 3/5; 170/400] START max_depth=5, min_samples_leaf=1, n_estimators=10........
[CV 3/5; 170/400] END max_depth=5, min_samples_leaf=1, n_estimators=10;, score=0.801 total time=   0.1s
[CV 4/5; 170/400] START max_depth=5, min_samples_leaf=1, n_estimators=10........
[CV 4/5; 170/400] END max_depth=5, min_samples_leaf=1, n_estimators=10;, score=0.799 total time=   0.1s
[CV 5/5; 170/400] START max_depth=5, min_samples_leaf=1, n_estimators=10........
[CV 5/5; 170/400] END max_depth=5, min_samples_leaf=1, n_estimators=10;, score=0.780 total time=   0.1s
[CV 1/5; 171/400] START max_depth=5, min_samples_leaf=2, n_estimators=1.........
[CV 1/5; 171/400] END max_depth=5, min_samples_leaf=2, n_estimators=1;, score=0.796 total time=   0.0s
[CV 2/5; 171/400] START max_depth=5, min_samples_leaf=2, n_estimators=1.........
[CV 2/5; 171/400] END max_depth=5, min_samples_leaf=2, n_estimators=1;, score=0.696 total time=   0.0s
[CV 3/5; 171/400] START max_depth=5, min_samples_leaf=2, n_estimators=1.........
[CV 3/5; 171/400] END max_depth=5, min_samples_leaf=2, n_estimators=1;, score=0.792 total time=   0.0s
[CV 4/5; 171/400] START max_depth=5, min_samples_leaf=2, n_estimators=1.........
[CV 4/5; 171/400] END max_depth=5, min_samples_leaf=2, n_estimators=1;, score=0.804 total time=   0.0s
[CV 5/5; 171/400] START max_depth=5, min_samples_leaf=2, n_estimators=1.........
[CV 5/5; 171/400] END max_depth=5, min_samples_leaf=2, n_estimators=1;, score=0.708 total time=   0.0s
[CV 1/5; 172/400] START max_depth=5, min_samples_leaf=2, n_estimators=2.........
[CV 1/5; 172/400] END max_depth=5, min_samples_leaf=2, n_estimators=2;, score=0.799 total time=   0.0s
[CV 2/5; 172/400] START max_depth=5, min_samples_leaf=2, n_estimators=2.........
[CV 2/5; 172/400] END max_depth=5, min_samples_leaf=2, n_estimators=2;, score=0.794 total time=   0.0s
[CV 3/5; 172/400] START max_depth=5, min_samples_leaf=2, n_estimators=2.........
[CV 3/5; 172/400] END max_depth=5, min_samples_leaf=2, n_estimators=2;, score=0.782 total time=   0.0s
[CV 4/5; 172/400] START max_depth=5, min_samples_leaf=2, n_estimators=2.........
[CV 4/5; 172/400] END max_depth=5, min_samples_leaf=2, n_estimators=2;, score=0.805 total time=   0.0s
[CV 5/5; 172/400] START max_depth=5, min_samples_leaf=2, n_estimators=2.........
[CV 5/5; 172/400] END max_depth=5, min_samples_leaf=2, n_estimators=2;, score=0.777 total time=   0.0s
[CV 1/5; 173/400] START max_depth=5, min_samples_leaf=2, n_estimators=3.........
[CV 1/5; 173/400] END max_depth=5, min_samples_leaf=2, n_estimators=3;, score=0.807 total time=   0.0s
[CV 2/5; 173/400] START max_depth=5, min_samples_leaf=2, n_estimators=3.........
[CV 2/5; 173/400] END max_depth=5, min_samples_leaf=2, n_estimators=3;, score=0.795 total time=   0.0s
[CV 3/5; 173/400] START max_depth=5, min_samples_leaf=2, n_estimators=3.........
[CV 3/5; 173/400] END max_depth=5, min_samples_leaf=2, n_estimators=3;, score=0.788 total time=   0.0s
[CV 4/5; 173/400] START max_depth=5, min_samples_leaf=2, n_estimators=3.........
[CV 4/5; 173/400] END max_depth=5, min_samples_leaf=2, n_estimators=3;, score=0.805 total time=   0.0s
[CV 5/5; 173/400] START max_depth=5, min_samples_leaf=2, n_estimators=3.........
[CV 5/5; 173/400] END max_depth=5, min_samples_leaf=2, n_estimators=3;, score=0.791 total time=   0.0s
[CV 1/5; 174/400] START max_depth=5, min_samples_leaf=2, n_estimators=4.........
[CV 1/5; 174/400] END max_depth=5, min_samples_leaf=2, n_estimators=4;, score=0.817 total time=   0.0s
[CV 2/5; 174/400] START max_depth=5, min_samples_leaf=2, n_estimators=4.........
[CV 2/5; 174/400] END max_depth=5, min_samples_leaf=2, n_estimators=4;, score=0.810 total time=   0.0s
[CV 3/5; 174/400] START max_depth=5, min_samples_leaf=2, n_estimators=4.........
[CV 3/5; 174/400] END max_depth=5, min_samples_leaf=2, n_estimators=4;, score=0.795 total time=   0.0s
[CV 4/5; 174/400] START max_depth=5, min_samples_leaf=2, n_estimators=4.........
[CV 4/5; 174/400] END max_depth=5, min_samples_leaf=2, n_estimators=4;, score=0.816 total time=   0.0s
[CV 5/5; 174/400] START max_depth=5, min_samples_leaf=2, n_estimators=4.........
[CV 5/5; 174/400] END max_depth=5, min_samples_leaf=2, n_estimators=4;, score=0.781 total time=   0.0s
[CV 1/5; 175/400] START max_depth=5, min_samples_leaf=2, n_estimators=5.........
[CV 1/5; 175/400] END max_depth=5, min_samples_leaf=2, n_estimators=5;, score=0.796 total time=   0.0s
[CV 2/5; 175/400] START max_depth=5, min_samples_leaf=2, n_estimators=5.........
[CV 2/5; 175/400] END max_depth=5, min_samples_leaf=2, n_estimators=5;, score=0.810 total time=   0.0s
[CV 3/5; 175/400] START max_depth=5, min_samples_leaf=2, n_estimators=5.........
[CV 3/5; 175/400] END max_depth=5, min_samples_leaf=2, n_estimators=5;, score=0.783 total time=   0.0s
[CV 4/5; 175/400] START max_depth=5, min_samples_leaf=2, n_estimators=5.........
[CV 4/5; 175/400] END max_depth=5, min_samples_leaf=2, n_estimators=5;, score=0.804 total time=   0.0s
[CV 5/5; 175/400] START max_depth=5, min_samples_leaf=2, n_estimators=5.........
[CV 5/5; 175/400] END max_depth=5, min_samples_leaf=2, n_estimators=5;, score=0.782 total time=   0.0s
[CV 1/5; 176/400] START max_depth=5, min_samples_leaf=2, n_estimators=6.........
[CV 1/5; 176/400] END max_depth=5, min_samples_leaf=2, n_estimators=6;, score=0.816 total time=   0.1s
[CV 2/5; 176/400] START max_depth=5, min_samples_leaf=2, n_estimators=6.........
[CV 2/5; 176/400] END max_depth=5, min_samples_leaf=2, n_estimators=6;, score=0.807 total time=   0.1s
[CV 3/5; 176/400] START max_depth=5, min_samples_leaf=2, n_estimators=6.........
[CV 3/5; 176/400] END max_depth=5, min_samples_leaf=2, n_estimators=6;, score=0.800 total time=   0.1s
[CV 4/5; 176/400] START max_depth=5, min_samples_leaf=2, n_estimators=6.........
[CV 4/5; 176/400] END max_depth=5, min_samples_leaf=2, n_estimators=6;, score=0.813 total time=   0.1s
[CV 5/5; 176/400] START max_depth=5, min_samples_leaf=2, n_estimators=6.........
[CV 5/5; 176/400] END max_depth=5, min_samples_leaf=2, n_estimators=6;, score=0.783 total time=   0.1s
[CV 1/5; 177/400] START max_depth=5, min_samples_leaf=2, n_estimators=7.........
[CV 1/5; 177/400] END max_depth=5, min_samples_leaf=2, n_estimators=7;, score=0.811 total time=   0.1s
[CV 2/5; 177/400] START max_depth=5, min_samples_leaf=2, n_estimators=7.........
[CV 2/5; 177/400] END max_depth=5, min_samples_leaf=2, n_estimators=7;, score=0.807 total time=   0.1s
[CV 3/5; 177/400] START max_depth=5, min_samples_leaf=2, n_estimators=7.........
[CV 3/5; 177/400] END max_depth=5, min_samples_leaf=2, n_estimators=7;, score=0.799 total time=   0.1s
[CV 4/5; 177/400] START max_depth=5, min_samples_leaf=2, n_estimators=7.........
[CV 4/5; 177/400] END max_depth=5, min_samples_leaf=2, n_estimators=7;, score=0.805 total time=   0.1s
[CV 5/5; 177/400] START max_depth=5, min_samples_leaf=2, n_estimators=7.........
[CV 5/5; 177/400] END max_depth=5, min_samples_leaf=2, n_estimators=7;, score=0.799 total time=   0.1s
[CV 1/5; 178/400] START max_depth=5, min_samples_leaf=2, n_estimators=8.........
[CV 1/5; 178/400] END max_depth=5, min_samples_leaf=2, n_estimators=8;, score=0.810 total time=   0.1s
[CV 2/5; 178/400] START max_depth=5, min_samples_leaf=2, n_estimators=8.........
[CV 2/5; 178/400] END max_depth=5, min_samples_leaf=2, n_estimators=8;, score=0.799 total time=   0.1s
[CV 3/5; 178/400] START max_depth=5, min_samples_leaf=2, n_estimators=8.........
[CV 3/5; 178/400] END max_depth=5, min_samples_leaf=2, n_estimators=8;, score=0.799 total time=   0.1s
[CV 4/5; 178/400] START max_depth=5, min_samples_leaf=2, n_estimators=8.........
[CV 4/5; 178/400] END max_depth=5, min_samples_leaf=2, n_estimators=8;, score=0.804 total time=   0.1s
[CV 5/5; 178/400] START max_depth=5, min_samples_leaf=2, n_estimators=8.........
[CV 5/5; 178/400] END max_depth=5, min_samples_leaf=2, n_estimators=8;, score=0.786 total time=   0.1s
[CV 1/5; 179/400] START max_depth=5, min_samples_leaf=2, n_estimators=9.........
[CV 1/5; 179/400] END max_depth=5, min_samples_leaf=2, n_estimators=9;, score=0.814 total time=   0.1s
[CV 2/5; 179/400] START max_depth=5, min_samples_leaf=2, n_estimators=9.........
[CV 2/5; 179/400] END max_depth=5, min_samples_leaf=2, n_estimators=9;, score=0.810 total time=   0.1s
[CV 3/5; 179/400] START max_depth=5, min_samples_leaf=2, n_estimators=9.........
[CV 3/5; 179/400] END max_depth=5, min_samples_leaf=2, n_estimators=9;, score=0.795 total time=   0.1s
[CV 4/5; 179/400] START max_depth=5, min_samples_leaf=2, n_estimators=9.........
[CV 4/5; 179/400] END max_depth=5, min_samples_leaf=2, n_estimators=9;, score=0.799 total time=   0.1s
[CV 5/5; 179/400] START max_depth=5, min_samples_leaf=2, n_estimators=9.........
[CV 5/5; 179/400] END max_depth=5, min_samples_leaf=2, n_estimators=9;, score=0.787 total time=   0.1s
[CV 1/5; 180/400] START max_depth=5, min_samples_leaf=2, n_estimators=10........
[CV 1/5; 180/400] END max_depth=5, min_samples_leaf=2, n_estimators=10;, score=0.811 total time=   0.1s
[CV 2/5; 180/400] START max_depth=5, min_samples_leaf=2, n_estimators=10........
[CV 2/5; 180/400] END max_depth=5, min_samples_leaf=2, n_estimators=10;, score=0.804 total time=   0.1s
[CV 3/5; 180/400] START max_depth=5, min_samples_leaf=2, n_estimators=10........
[CV 3/5; 180/400] END max_depth=5, min_samples_leaf=2, n_estimators=10;, score=0.797 total time=   0.1s
[CV 4/5; 180/400] START max_depth=5, min_samples_leaf=2, n_estimators=10........
[CV 4/5; 180/400] END max_depth=5, min_samples_leaf=2, n_estimators=10;, score=0.809 total time=   0.1s
[CV 5/5; 180/400] START max_depth=5, min_samples_leaf=2, n_estimators=10........
[CV 5/5; 180/400] END max_depth=5, min_samples_leaf=2, n_estimators=10;, score=0.795 total time=   0.1s
[CV 1/5; 181/400] START max_depth=5, min_samples_leaf=3, n_estimators=1.........
[CV 1/5; 181/400] END max_depth=5, min_samples_leaf=3, n_estimators=1;, score=0.725 total time=   0.0s
[CV 2/5; 181/400] START max_depth=5, min_samples_leaf=3, n_estimators=1.........
[CV 2/5; 181/400] END max_depth=5, min_samples_leaf=3, n_estimators=1;, score=0.795 total time=   0.0s
[CV 3/5; 181/400] START max_depth=5, min_samples_leaf=3, n_estimators=1.........
[CV 3/5; 181/400] END max_depth=5, min_samples_leaf=3, n_estimators=1;, score=0.763 total time=   0.0s
[CV 4/5; 181/400] START max_depth=5, min_samples_leaf=3, n_estimators=1.........
[CV 4/5; 181/400] END max_depth=5, min_samples_leaf=3, n_estimators=1;, score=0.785 total time=   0.0s
[CV 5/5; 181/400] START max_depth=5, min_samples_leaf=3, n_estimators=1.........
[CV 5/5; 181/400] END max_depth=5, min_samples_leaf=3, n_estimators=1;, score=0.774 total time=   0.0s
[CV 1/5; 182/400] START max_depth=5, min_samples_leaf=3, n_estimators=2.........
[CV 1/5; 182/400] END max_depth=5, min_samples_leaf=3, n_estimators=2;, score=0.801 total time=   0.0s
[CV 2/5; 182/400] START max_depth=5, min_samples_leaf=3, n_estimators=2.........
[CV 2/5; 182/400] END max_depth=5, min_samples_leaf=3, n_estimators=2;, score=0.798 total time=   0.0s
[CV 3/5; 182/400] START max_depth=5, min_samples_leaf=3, n_estimators=2.........
[CV 3/5; 182/400] END max_depth=5, min_samples_leaf=3, n_estimators=2;, score=0.790 total time=   0.0s
[CV 4/5; 182/400] START max_depth=5, min_samples_leaf=3, n_estimators=2.........
[CV 4/5; 182/400] END max_depth=5, min_samples_leaf=3, n_estimators=2;, score=0.802 total time=   0.0s
[CV 5/5; 182/400] START max_depth=5, min_samples_leaf=3, n_estimators=2.........
[CV 5/5; 182/400] END max_depth=5, min_samples_leaf=3, n_estimators=2;, score=0.775 total time=   0.0s
[CV 1/5; 183/400] START max_depth=5, min_samples_leaf=3, n_estimators=3.........
[CV 1/5; 183/400] END max_depth=5, min_samples_leaf=3, n_estimators=3;, score=0.811 total time=   0.0s
[CV 2/5; 183/400] START max_depth=5, min_samples_leaf=3, n_estimators=3.........
[CV 2/5; 183/400] END max_depth=5, min_samples_leaf=3, n_estimators=3;, score=0.795 total time=   0.0s
[CV 3/5; 183/400] START max_depth=5, min_samples_leaf=3, n_estimators=3.........
[CV 3/5; 183/400] END max_depth=5, min_samples_leaf=3, n_estimators=3;, score=0.786 total time=   0.0s
[CV 4/5; 183/400] START max_depth=5, min_samples_leaf=3, n_estimators=3.........
[CV 4/5; 183/400] END max_depth=5, min_samples_leaf=3, n_estimators=3;, score=0.804 total time=   0.0s
[CV 5/5; 183/400] START max_depth=5, min_samples_leaf=3, n_estimators=3.........
[CV 5/5; 183/400] END max_depth=5, min_samples_leaf=3, n_estimators=3;, score=0.785 total time=   0.0s
[CV 1/5; 184/400] START max_depth=5, min_samples_leaf=3, n_estimators=4.........
[CV 1/5; 184/400] END max_depth=5, min_samples_leaf=3, n_estimators=4;, score=0.798 total time=   0.0s
[CV 2/5; 184/400] START max_depth=5, min_samples_leaf=3, n_estimators=4.........
[CV 2/5; 184/400] END max_depth=5, min_samples_leaf=3, n_estimators=4;, score=0.813 total time=   0.0s
[CV 3/5; 184/400] START max_depth=5, min_samples_leaf=3, n_estimators=4.........
[CV 3/5; 184/400] END max_depth=5, min_samples_leaf=3, n_estimators=4;, score=0.804 total time=   0.0s
[CV 4/5; 184/400] START max_depth=5, min_samples_leaf=3, n_estimators=4.........
[CV 4/5; 184/400] END max_depth=5, min_samples_leaf=3, n_estimators=4;, score=0.802 total time=   0.0s
[CV 5/5; 184/400] START max_depth=5, min_samples_leaf=3, n_estimators=4.........
[CV 5/5; 184/400] END max_depth=5, min_samples_leaf=3, n_estimators=4;, score=0.800 total time=   0.0s
[CV 1/5; 185/400] START max_depth=5, min_samples_leaf=3, n_estimators=5.........
[CV 1/5; 185/400] END max_depth=5, min_samples_leaf=3, n_estimators=5;, score=0.805 total time=   0.0s
[CV 2/5; 185/400] START max_depth=5, min_samples_leaf=3, n_estimators=5.........
[CV 2/5; 185/400] END max_depth=5, min_samples_leaf=3, n_estimators=5;, score=0.795 total time=   0.0s
[CV 3/5; 185/400] START max_depth=5, min_samples_leaf=3, n_estimators=5.........
[CV 3/5; 185/400] END max_depth=5, min_samples_leaf=3, n_estimators=5;, score=0.794 total time=   0.0s
[CV 4/5; 185/400] START max_depth=5, min_samples_leaf=3, n_estimators=5.........
[CV 4/5; 185/400] END max_depth=5, min_samples_leaf=3, n_estimators=5;, score=0.810 total time=   0.1s
[CV 5/5; 185/400] START max_depth=5, min_samples_leaf=3, n_estimators=5.........
[CV 5/5; 185/400] END max_depth=5, min_samples_leaf=3, n_estimators=5;, score=0.784 total time=   0.0s
[CV 1/5; 186/400] START max_depth=5, min_samples_leaf=3, n_estimators=6.........
[CV 1/5; 186/400] END max_depth=5, min_samples_leaf=3, n_estimators=6;, score=0.803 total time=   0.1s
[CV 2/5; 186/400] START max_depth=5, min_samples_leaf=3, n_estimators=6.........
[CV 2/5; 186/400] END max_depth=5, min_samples_leaf=3, n_estimators=6;, score=0.797 total time=   0.1s
[CV 3/5; 186/400] START max_depth=5, min_samples_leaf=3, n_estimators=6.........
[CV 3/5; 186/400] END max_depth=5, min_samples_leaf=3, n_estimators=6;, score=0.792 total time=   0.0s
[CV 4/5; 186/400] START max_depth=5, min_samples_leaf=3, n_estimators=6.........
[CV 4/5; 186/400] END max_depth=5, min_samples_leaf=3, n_estimators=6;, score=0.810 total time=   0.0s
[CV 5/5; 186/400] START max_depth=5, min_samples_leaf=3, n_estimators=6.........
[CV 5/5; 186/400] END max_depth=5, min_samples_leaf=3, n_estimators=6;, score=0.790 total time=   0.0s
[CV 1/5; 187/400] START max_depth=5, min_samples_leaf=3, n_estimators=7.........
[CV 1/5; 187/400] END max_depth=5, min_samples_leaf=3, n_estimators=7;, score=0.810 total time=   0.0s
[CV 2/5; 187/400] START max_depth=5, min_samples_leaf=3, n_estimators=7.........
[CV 2/5; 187/400] END max_depth=5, min_samples_leaf=3, n_estimators=7;, score=0.813 total time=   0.0s
[CV 3/5; 187/400] START max_depth=5, min_samples_leaf=3, n_estimators=7.........
[CV 3/5; 187/400] END max_depth=5, min_samples_leaf=3, n_estimators=7;, score=0.811 total time=   0.0s
[CV 4/5; 187/400] START max_depth=5, min_samples_leaf=3, n_estimators=7.........
[CV 4/5; 187/400] END max_depth=5, min_samples_leaf=3, n_estimators=7;, score=0.808 total time=   0.0s
[CV 5/5; 187/400] START max_depth=5, min_samples_leaf=3, n_estimators=7.........
[CV 5/5; 187/400] END max_depth=5, min_samples_leaf=3, n_estimators=7;, score=0.799 total time=   0.0s
[CV 1/5; 188/400] START max_depth=5, min_samples_leaf=3, n_estimators=8.........
[CV 1/5; 188/400] END max_depth=5, min_samples_leaf=3, n_estimators=8;, score=0.808 total time=   0.1s
[CV 2/5; 188/400] START max_depth=5, min_samples_leaf=3, n_estimators=8.........
[CV 2/5; 188/400] END max_depth=5, min_samples_leaf=3, n_estimators=8;, score=0.802 total time=   0.0s
[CV 3/5; 188/400] START max_depth=5, min_samples_leaf=3, n_estimators=8.........
[CV 3/5; 188/400] END max_depth=5, min_samples_leaf=3, n_estimators=8;, score=0.805 total time=   0.0s
[CV 4/5; 188/400] START max_depth=5, min_samples_leaf=3, n_estimators=8.........
[CV 4/5; 188/400] END max_depth=5, min_samples_leaf=3, n_estimators=8;, score=0.805 total time=   0.0s
[CV 5/5; 188/400] START max_depth=5, min_samples_leaf=3, n_estimators=8.........
[CV 5/5; 188/400] END max_depth=5, min_samples_leaf=3, n_estimators=8;, score=0.785 total time=   0.0s
[CV 1/5; 189/400] START max_depth=5, min_samples_leaf=3, n_estimators=9.........
[CV 1/5; 189/400] END max_depth=5, min_samples_leaf=3, n_estimators=9;, score=0.808 total time=   0.0s
[CV 2/5; 189/400] START max_depth=5, min_samples_leaf=3, n_estimators=9.........
[CV 2/5; 189/400] END max_depth=5, min_samples_leaf=3, n_estimators=9;, score=0.801 total time=   0.0s
[CV 3/5; 189/400] START max_depth=5, min_samples_leaf=3, n_estimators=9.........
[CV 3/5; 189/400] END max_depth=5, min_samples_leaf=3, n_estimators=9;, score=0.800 total time=   0.0s
[CV 4/5; 189/400] START max_depth=5, min_samples_leaf=3, n_estimators=9.........
[CV 4/5; 189/400] END max_depth=5, min_samples_leaf=3, n_estimators=9;, score=0.807 total time=   0.1s
[CV 5/5; 189/400] START max_depth=5, min_samples_leaf=3, n_estimators=9.........
[CV 5/5; 189/400] END max_depth=5, min_samples_leaf=3, n_estimators=9;, score=0.783 total time=   0.1s
[CV 1/5; 190/400] START max_depth=5, min_samples_leaf=3, n_estimators=10........
[CV 1/5; 190/400] END max_depth=5, min_samples_leaf=3, n_estimators=10;, score=0.805 total time=   0.1s
[CV 2/5; 190/400] START max_depth=5, min_samples_leaf=3, n_estimators=10........
[CV 2/5; 190/400] END max_depth=5, min_samples_leaf=3, n_estimators=10;, score=0.806 total time=   0.1s
[CV 3/5; 190/400] START max_depth=5, min_samples_leaf=3, n_estimators=10........
[CV 3/5; 190/400] END max_depth=5, min_samples_leaf=3, n_estimators=10;, score=0.793 total time=   0.1s
[CV 4/5; 190/400] START max_depth=5, min_samples_leaf=3, n_estimators=10........
[CV 4/5; 190/400] END max_depth=5, min_samples_leaf=3, n_estimators=10;, score=0.805 total time=   0.1s
[CV 5/5; 190/400] START max_depth=5, min_samples_leaf=3, n_estimators=10........
[CV 5/5; 190/400] END max_depth=5, min_samples_leaf=3, n_estimators=10;, score=0.788 total time=   0.1s
[CV 1/5; 191/400] START max_depth=5, min_samples_leaf=4, n_estimators=1.........
[CV 1/5; 191/400] END max_depth=5, min_samples_leaf=4, n_estimators=1;, score=0.758 total time=   0.0s
[CV 2/5; 191/400] START max_depth=5, min_samples_leaf=4, n_estimators=1.........
[CV 2/5; 191/400] END max_depth=5, min_samples_leaf=4, n_estimators=1;, score=0.784 total time=   0.0s
[CV 3/5; 191/400] START max_depth=5, min_samples_leaf=4, n_estimators=1.........
[CV 3/5; 191/400] END max_depth=5, min_samples_leaf=4, n_estimators=1;, score=0.782 total time=   0.0s
[CV 4/5; 191/400] START max_depth=5, min_samples_leaf=4, n_estimators=1.........
[CV 4/5; 191/400] END max_depth=5, min_samples_leaf=4, n_estimators=1;, score=0.780 total time=   0.0s
[CV 5/5; 191/400] START max_depth=5, min_samples_leaf=4, n_estimators=1.........
[CV 5/5; 191/400] END max_depth=5, min_samples_leaf=4, n_estimators=1;, score=0.760 total time=   0.0s
[CV 1/5; 192/400] START max_depth=5, min_samples_leaf=4, n_estimators=2.........
[CV 1/5; 192/400] END max_depth=5, min_samples_leaf=4, n_estimators=2;, score=0.807 total time=   0.0s
[CV 2/5; 192/400] START max_depth=5, min_samples_leaf=4, n_estimators=2.........
[CV 2/5; 192/400] END max_depth=5, min_samples_leaf=4, n_estimators=2;, score=0.791 total time=   0.0s
[CV 3/5; 192/400] START max_depth=5, min_samples_leaf=4, n_estimators=2.........
[CV 3/5; 192/400] END max_depth=5, min_samples_leaf=4, n_estimators=2;, score=0.799 total time=   0.0s
[CV 4/5; 192/400] START max_depth=5, min_samples_leaf=4, n_estimators=2.........
[CV 4/5; 192/400] END max_depth=5, min_samples_leaf=4, n_estimators=2;, score=0.799 total time=   0.0s
[CV 5/5; 192/400] START max_depth=5, min_samples_leaf=4, n_estimators=2.........
[CV 5/5; 192/400] END max_depth=5, min_samples_leaf=4, n_estimators=2;, score=0.783 total time=   0.0s
[CV 1/5; 193/400] START max_depth=5, min_samples_leaf=4, n_estimators=3.........
[CV 1/5; 193/400] END max_depth=5, min_samples_leaf=4, n_estimators=3;, score=0.805 total time=   0.0s
[CV 2/5; 193/400] START max_depth=5, min_samples_leaf=4, n_estimators=3.........
[CV 2/5; 193/400] END max_depth=5, min_samples_leaf=4, n_estimators=3;, score=0.817 total time=   0.0s
[CV 3/5; 193/400] START max_depth=5, min_samples_leaf=4, n_estimators=3.........
[CV 3/5; 193/400] END max_depth=5, min_samples_leaf=4, n_estimators=3;, score=0.804 total time=   0.0s
[CV 4/5; 193/400] START max_depth=5, min_samples_leaf=4, n_estimators=3.........
[CV 4/5; 193/400] END max_depth=5, min_samples_leaf=4, n_estimators=3;, score=0.808 total time=   0.0s
[CV 5/5; 193/400] START max_depth=5, min_samples_leaf=4, n_estimators=3.........
[CV 5/5; 193/400] END max_depth=5, min_samples_leaf=4, n_estimators=3;, score=0.802 total time=   0.0s
[CV 1/5; 194/400] START max_depth=5, min_samples_leaf=4, n_estimators=4.........
[CV 1/5; 194/400] END max_depth=5, min_samples_leaf=4, n_estimators=4;, score=0.802 total time=   0.0s
[CV 2/5; 194/400] START max_depth=5, min_samples_leaf=4, n_estimators=4.........
[CV 2/5; 194/400] END max_depth=5, min_samples_leaf=4, n_estimators=4;, score=0.805 total time=   0.0s
[CV 3/5; 194/400] START max_depth=5, min_samples_leaf=4, n_estimators=4.........
[CV 3/5; 194/400] END max_depth=5, min_samples_leaf=4, n_estimators=4;, score=0.783 total time=   0.0s
[CV 4/5; 194/400] START max_depth=5, min_samples_leaf=4, n_estimators=4.........
[CV 4/5; 194/400] END max_depth=5, min_samples_leaf=4, n_estimators=4;, score=0.795 total time=   0.0s
[CV 5/5; 194/400] START max_depth=5, min_samples_leaf=4, n_estimators=4.........
[CV 5/5; 194/400] END max_depth=5, min_samples_leaf=4, n_estimators=4;, score=0.796 total time=   0.0s
[CV 1/5; 195/400] START max_depth=5, min_samples_leaf=4, n_estimators=5.........
[CV 1/5; 195/400] END max_depth=5, min_samples_leaf=4, n_estimators=5;, score=0.815 total time=   0.0s
[CV 2/5; 195/400] START max_depth=5, min_samples_leaf=4, n_estimators=5.........
[CV 2/5; 195/400] END max_depth=5, min_samples_leaf=4, n_estimators=5;, score=0.806 total time=   0.0s
[CV 3/5; 195/400] START max_depth=5, min_samples_leaf=4, n_estimators=5.........
[CV 3/5; 195/400] END max_depth=5, min_samples_leaf=4, n_estimators=5;, score=0.813 total time=   0.0s
[CV 4/5; 195/400] START max_depth=5, min_samples_leaf=4, n_estimators=5.........
[CV 4/5; 195/400] END max_depth=5, min_samples_leaf=4, n_estimators=5;, score=0.803 total time=   0.0s
[CV 5/5; 195/400] START max_depth=5, min_samples_leaf=4, n_estimators=5.........
[CV 5/5; 195/400] END max_depth=5, min_samples_leaf=4, n_estimators=5;, score=0.791 total time=   0.0s
[CV 1/5; 196/400] START max_depth=5, min_samples_leaf=4, n_estimators=6.........
[CV 1/5; 196/400] END max_depth=5, min_samples_leaf=4, n_estimators=6;, score=0.813 total time=   0.0s
[CV 2/5; 196/400] START max_depth=5, min_samples_leaf=4, n_estimators=6.........
[CV 2/5; 196/400] END max_depth=5, min_samples_leaf=4, n_estimators=6;, score=0.802 total time=   0.0s
[CV 3/5; 196/400] START max_depth=5, min_samples_leaf=4, n_estimators=6.........
[CV 3/5; 196/400] END max_depth=5, min_samples_leaf=4, n_estimators=6;, score=0.801 total time=   0.0s
[CV 4/5; 196/400] START max_depth=5, min_samples_leaf=4, n_estimators=6.........
[CV 4/5; 196/400] END max_depth=5, min_samples_leaf=4, n_estimators=6;, score=0.807 total time=   0.0s
[CV 5/5; 196/400] START max_depth=5, min_samples_leaf=4, n_estimators=6.........
[CV 5/5; 196/400] END max_depth=5, min_samples_leaf=4, n_estimators=6;, score=0.794 total time=   0.0s
[CV 1/5; 197/400] START max_depth=5, min_samples_leaf=4, n_estimators=7.........
[CV 1/5; 197/400] END max_depth=5, min_samples_leaf=4, n_estimators=7;, score=0.807 total time=   0.0s
[CV 2/5; 197/400] START max_depth=5, min_samples_leaf=4, n_estimators=7.........
[CV 2/5; 197/400] END max_depth=5, min_samples_leaf=4, n_estimators=7;, score=0.806 total time=   0.0s
[CV 3/5; 197/400] START max_depth=5, min_samples_leaf=4, n_estimators=7.........
[CV 3/5; 197/400] END max_depth=5, min_samples_leaf=4, n_estimators=7;, score=0.802 total time=   0.0s
[CV 4/5; 197/400] START max_depth=5, min_samples_leaf=4, n_estimators=7.........
[CV 4/5; 197/400] END max_depth=5, min_samples_leaf=4, n_estimators=7;, score=0.817 total time=   0.0s
[CV 5/5; 197/400] START max_depth=5, min_samples_leaf=4, n_estimators=7.........
[CV 5/5; 197/400] END max_depth=5, min_samples_leaf=4, n_estimators=7;, score=0.800 total time=   0.0s
[CV 1/5; 198/400] START max_depth=5, min_samples_leaf=4, n_estimators=8.........
[CV 1/5; 198/400] END max_depth=5, min_samples_leaf=4, n_estimators=8;, score=0.822 total time=   0.1s
[CV 2/5; 198/400] START max_depth=5, min_samples_leaf=4, n_estimators=8.........
[CV 2/5; 198/400] END max_depth=5, min_samples_leaf=4, n_estimators=8;, score=0.812 total time=   0.0s
[CV 3/5; 198/400] START max_depth=5, min_samples_leaf=4, n_estimators=8.........
[CV 3/5; 198/400] END max_depth=5, min_samples_leaf=4, n_estimators=8;, score=0.795 total time=   0.0s
[CV 4/5; 198/400] START max_depth=5, min_samples_leaf=4, n_estimators=8.........
[CV 4/5; 198/400] END max_depth=5, min_samples_leaf=4, n_estimators=8;, score=0.802 total time=   0.0s
[CV 5/5; 198/400] START max_depth=5, min_samples_leaf=4, n_estimators=8.........
[CV 5/5; 198/400] END max_depth=5, min_samples_leaf=4, n_estimators=8;, score=0.787 total time=   0.0s
[CV 1/5; 199/400] START max_depth=5, min_samples_leaf=4, n_estimators=9.........
[CV 1/5; 199/400] END max_depth=5, min_samples_leaf=4, n_estimators=9;, score=0.812 total time=   0.1s
[CV 2/5; 199/400] START max_depth=5, min_samples_leaf=4, n_estimators=9.........
[CV 2/5; 199/400] END max_depth=5, min_samples_leaf=4, n_estimators=9;, score=0.808 total time=   0.1s
[CV 3/5; 199/400] START max_depth=5, min_samples_leaf=4, n_estimators=9.........
[CV 3/5; 199/400] END max_depth=5, min_samples_leaf=4, n_estimators=9;, score=0.791 total time=   0.1s
[CV 4/5; 199/400] START max_depth=5, min_samples_leaf=4, n_estimators=9.........
[CV 4/5; 199/400] END max_depth=5, min_samples_leaf=4, n_estimators=9;, score=0.807 total time=   0.0s
[CV 5/5; 199/400] START max_depth=5, min_samples_leaf=4, n_estimators=9.........
[CV 5/5; 199/400] END max_depth=5, min_samples_leaf=4, n_estimators=9;, score=0.783 total time=   0.1s
[CV 1/5; 200/400] START max_depth=5, min_samples_leaf=4, n_estimators=10........
[CV 1/5; 200/400] END max_depth=5, min_samples_leaf=4, n_estimators=10;, score=0.812 total time=   0.1s
[CV 2/5; 200/400] START max_depth=5, min_samples_leaf=4, n_estimators=10........
[CV 2/5; 200/400] END max_depth=5, min_samples_leaf=4, n_estimators=10;, score=0.805 total time=   0.1s
[CV 3/5; 200/400] START max_depth=5, min_samples_leaf=4, n_estimators=10........
[CV 3/5; 200/400] END max_depth=5, min_samples_leaf=4, n_estimators=10;, score=0.796 total time=   0.1s
[CV 4/5; 200/400] START max_depth=5, min_samples_leaf=4, n_estimators=10........
[CV 4/5; 200/400] END max_depth=5, min_samples_leaf=4, n_estimators=10;, score=0.803 total time=   0.1s
[CV 5/5; 200/400] START max_depth=5, min_samples_leaf=4, n_estimators=10........
[CV 5/5; 200/400] END max_depth=5, min_samples_leaf=4, n_estimators=10;, score=0.797 total time=   0.1s
[CV 1/5; 201/400] START max_depth=6, min_samples_leaf=1, n_estimators=1.........
[CV 1/5; 201/400] END max_depth=6, min_samples_leaf=1, n_estimators=1;, score=0.793 total time=   0.0s
[CV 2/5; 201/400] START max_depth=6, min_samples_leaf=1, n_estimators=1.........
[CV 2/5; 201/400] END max_depth=6, min_samples_leaf=1, n_estimators=1;, score=0.791 total time=   0.0s
[CV 3/5; 201/400] START max_depth=6, min_samples_leaf=1, n_estimators=1.........
[CV 3/5; 201/400] END max_depth=6, min_samples_leaf=1, n_estimators=1;, score=0.799 total time=   0.0s
[CV 4/5; 201/400] START max_depth=6, min_samples_leaf=1, n_estimators=1.........
[CV 4/5; 201/400] END max_depth=6, min_samples_leaf=1, n_estimators=1;, score=0.767 total time=   0.0s
[CV 5/5; 201/400] START max_depth=6, min_samples_leaf=1, n_estimators=1.........
[CV 5/5; 201/400] END max_depth=6, min_samples_leaf=1, n_estimators=1;, score=0.796 total time=   0.0s
[CV 1/5; 202/400] START max_depth=6, min_samples_leaf=1, n_estimators=2.........
[CV 1/5; 202/400] END max_depth=6, min_samples_leaf=1, n_estimators=2;, score=0.802 total time=   0.0s
[CV 2/5; 202/400] START max_depth=6, min_samples_leaf=1, n_estimators=2.........
[CV 2/5; 202/400] END max_depth=6, min_samples_leaf=1, n_estimators=2;, score=0.805 total time=   0.0s
[CV 3/5; 202/400] START max_depth=6, min_samples_leaf=1, n_estimators=2.........
[CV 3/5; 202/400] END max_depth=6, min_samples_leaf=1, n_estimators=2;, score=0.790 total time=   0.0s
[CV 4/5; 202/400] START max_depth=6, min_samples_leaf=1, n_estimators=2.........
[CV 4/5; 202/400] END max_depth=6, min_samples_leaf=1, n_estimators=2;, score=0.807 total time=   0.0s
[CV 5/5; 202/400] START max_depth=6, min_samples_leaf=1, n_estimators=2.........
[CV 5/5; 202/400] END max_depth=6, min_samples_leaf=1, n_estimators=2;, score=0.796 total time=   0.0s
[CV 1/5; 203/400] START max_depth=6, min_samples_leaf=1, n_estimators=3.........
[CV 1/5; 203/400] END max_depth=6, min_samples_leaf=1, n_estimators=3;, score=0.809 total time=   0.0s
[CV 2/5; 203/400] START max_depth=6, min_samples_leaf=1, n_estimators=3.........
[CV 2/5; 203/400] END max_depth=6, min_samples_leaf=1, n_estimators=3;, score=0.828 total time=   0.0s
[CV 3/5; 203/400] START max_depth=6, min_samples_leaf=1, n_estimators=3.........
[CV 3/5; 203/400] END max_depth=6, min_samples_leaf=1, n_estimators=3;, score=0.804 total time=   0.0s
[CV 4/5; 203/400] START max_depth=6, min_samples_leaf=1, n_estimators=3.........
[CV 4/5; 203/400] END max_depth=6, min_samples_leaf=1, n_estimators=3;, score=0.824 total time=   0.0s
[CV 5/5; 203/400] START max_depth=6, min_samples_leaf=1, n_estimators=3.........
[CV 5/5; 203/400] END max_depth=6, min_samples_leaf=1, n_estimators=3;, score=0.791 total time=   0.0s
[CV 1/5; 204/400] START max_depth=6, min_samples_leaf=1, n_estimators=4.........
[CV 1/5; 204/400] END max_depth=6, min_samples_leaf=1, n_estimators=4;, score=0.818 total time=   0.0s
[CV 2/5; 204/400] START max_depth=6, min_samples_leaf=1, n_estimators=4.........
[CV 2/5; 204/400] END max_depth=6, min_samples_leaf=1, n_estimators=4;, score=0.821 total time=   0.0s
[CV 3/5; 204/400] START max_depth=6, min_samples_leaf=1, n_estimators=4.........
[CV 3/5; 204/400] END max_depth=6, min_samples_leaf=1, n_estimators=4;, score=0.812 total time=   0.0s
[CV 4/5; 204/400] START max_depth=6, min_samples_leaf=1, n_estimators=4.........
[CV 4/5; 204/400] END max_depth=6, min_samples_leaf=1, n_estimators=4;, score=0.810 total time=   0.0s
[CV 5/5; 204/400] START max_depth=6, min_samples_leaf=1, n_estimators=4.........
[CV 5/5; 204/400] END max_depth=6, min_samples_leaf=1, n_estimators=4;, score=0.810 total time=   0.0s
[CV 1/5; 205/400] START max_depth=6, min_samples_leaf=1, n_estimators=5.........
[CV 1/5; 205/400] END max_depth=6, min_samples_leaf=1, n_estimators=5;, score=0.808 total time=   0.0s
[CV 2/5; 205/400] START max_depth=6, min_samples_leaf=1, n_estimators=5.........
[CV 2/5; 205/400] END max_depth=6, min_samples_leaf=1, n_estimators=5;, score=0.815 total time=   0.0s
[CV 3/5; 205/400] START max_depth=6, min_samples_leaf=1, n_estimators=5.........
[CV 3/5; 205/400] END max_depth=6, min_samples_leaf=1, n_estimators=5;, score=0.794 total time=   0.0s
[CV 4/5; 205/400] START max_depth=6, min_samples_leaf=1, n_estimators=5.........
[CV 4/5; 205/400] END max_depth=6, min_samples_leaf=1, n_estimators=5;, score=0.805 total time=   0.0s
[CV 5/5; 205/400] START max_depth=6, min_samples_leaf=1, n_estimators=5.........
[CV 5/5; 205/400] END max_depth=6, min_samples_leaf=1, n_estimators=5;, score=0.805 total time=   0.0s
[CV 1/5; 206/400] START max_depth=6, min_samples_leaf=1, n_estimators=6.........
[CV 1/5; 206/400] END max_depth=6, min_samples_leaf=1, n_estimators=6;, score=0.819 total time=   0.0s
[CV 2/5; 206/400] START max_depth=6, min_samples_leaf=1, n_estimators=6.........
[CV 2/5; 206/400] END max_depth=6, min_samples_leaf=1, n_estimators=6;, score=0.819 total time=   0.0s
[CV 3/5; 206/400] START max_depth=6, min_samples_leaf=1, n_estimators=6.........
[CV 3/5; 206/400] END max_depth=6, min_samples_leaf=1, n_estimators=6;, score=0.822 total time=   0.0s
[CV 4/5; 206/400] START max_depth=6, min_samples_leaf=1, n_estimators=6.........
[CV 4/5; 206/400] END max_depth=6, min_samples_leaf=1, n_estimators=6;, score=0.813 total time=   0.0s
[CV 5/5; 206/400] START max_depth=6, min_samples_leaf=1, n_estimators=6.........
[CV 5/5; 206/400] END max_depth=6, min_samples_leaf=1, n_estimators=6;, score=0.803 total time=   0.0s
[CV 1/5; 207/400] START max_depth=6, min_samples_leaf=1, n_estimators=7.........
[CV 1/5; 207/400] END max_depth=6, min_samples_leaf=1, n_estimators=7;, score=0.823 total time=   0.0s
[CV 2/5; 207/400] START max_depth=6, min_samples_leaf=1, n_estimators=7.........
[CV 2/5; 207/400] END max_depth=6, min_samples_leaf=1, n_estimators=7;, score=0.817 total time=   0.0s
[CV 3/5; 207/400] START max_depth=6, min_samples_leaf=1, n_estimators=7.........
[CV 3/5; 207/400] END max_depth=6, min_samples_leaf=1, n_estimators=7;, score=0.807 total time=   0.0s
[CV 4/5; 207/400] START max_depth=6, min_samples_leaf=1, n_estimators=7.........
[CV 4/5; 207/400] END max_depth=6, min_samples_leaf=1, n_estimators=7;, score=0.807 total time=   0.0s
[CV 5/5; 207/400] START max_depth=6, min_samples_leaf=1, n_estimators=7.........
[CV 5/5; 207/400] END max_depth=6, min_samples_leaf=1, n_estimators=7;, score=0.805 total time=   0.1s
[CV 1/5; 208/400] START max_depth=6, min_samples_leaf=1, n_estimators=8.........
[CV 1/5; 208/400] END max_depth=6, min_samples_leaf=1, n_estimators=8;, score=0.820 total time=   0.1s
[CV 2/5; 208/400] START max_depth=6, min_samples_leaf=1, n_estimators=8.........
[CV 2/5; 208/400] END max_depth=6, min_samples_leaf=1, n_estimators=8;, score=0.830 total time=   0.1s
[CV 3/5; 208/400] START max_depth=6, min_samples_leaf=1, n_estimators=8.........
[CV 3/5; 208/400] END max_depth=6, min_samples_leaf=1, n_estimators=8;, score=0.815 total time=   0.1s
[CV 4/5; 208/400] START max_depth=6, min_samples_leaf=1, n_estimators=8.........
[CV 4/5; 208/400] END max_depth=6, min_samples_leaf=1, n_estimators=8;, score=0.811 total time=   0.1s
[CV 5/5; 208/400] START max_depth=6, min_samples_leaf=1, n_estimators=8.........
[CV 5/5; 208/400] END max_depth=6, min_samples_leaf=1, n_estimators=8;, score=0.819 total time=   0.1s
[CV 1/5; 209/400] START max_depth=6, min_samples_leaf=1, n_estimators=9.........
[CV 1/5; 209/400] END max_depth=6, min_samples_leaf=1, n_estimators=9;, score=0.824 total time=   0.1s
[CV 2/5; 209/400] START max_depth=6, min_samples_leaf=1, n_estimators=9.........
[CV 2/5; 209/400] END max_depth=6, min_samples_leaf=1, n_estimators=9;, score=0.821 total time=   0.1s
[CV 3/5; 209/400] START max_depth=6, min_samples_leaf=1, n_estimators=9.........
[CV 3/5; 209/400] END max_depth=6, min_samples_leaf=1, n_estimators=9;, score=0.810 total time=   0.1s
[CV 4/5; 209/400] START max_depth=6, min_samples_leaf=1, n_estimators=9.........
[CV 4/5; 209/400] END max_depth=6, min_samples_leaf=1, n_estimators=9;, score=0.818 total time=   0.1s
[CV 5/5; 209/400] START max_depth=6, min_samples_leaf=1, n_estimators=9.........
[CV 5/5; 209/400] END max_depth=6, min_samples_leaf=1, n_estimators=9;, score=0.810 total time=   0.1s
[CV 1/5; 210/400] START max_depth=6, min_samples_leaf=1, n_estimators=10........
[CV 1/5; 210/400] END max_depth=6, min_samples_leaf=1, n_estimators=10;, score=0.827 total time=   0.1s
[CV 2/5; 210/400] START max_depth=6, min_samples_leaf=1, n_estimators=10........
[CV 2/5; 210/400] END max_depth=6, min_samples_leaf=1, n_estimators=10;, score=0.832 total time=   0.1s
[CV 3/5; 210/400] START max_depth=6, min_samples_leaf=1, n_estimators=10........
[CV 3/5; 210/400] END max_depth=6, min_samples_leaf=1, n_estimators=10;, score=0.807 total time=   0.1s
[CV 4/5; 210/400] START max_depth=6, min_samples_leaf=1, n_estimators=10........
[CV 4/5; 210/400] END max_depth=6, min_samples_leaf=1, n_estimators=10;, score=0.821 total time=   0.1s
[CV 5/5; 210/400] START max_depth=6, min_samples_leaf=1, n_estimators=10........
[CV 5/5; 210/400] END max_depth=6, min_samples_leaf=1, n_estimators=10;, score=0.798 total time=   0.1s
[CV 1/5; 211/400] START max_depth=6, min_samples_leaf=2, n_estimators=1.........
[CV 1/5; 211/400] END max_depth=6, min_samples_leaf=2, n_estimators=1;, score=0.759 total time=   0.0s
[CV 2/5; 211/400] START max_depth=6, min_samples_leaf=2, n_estimators=1.........
[CV 2/5; 211/400] END max_depth=6, min_samples_leaf=2, n_estimators=1;, score=0.807 total time=   0.0s
[CV 3/5; 211/400] START max_depth=6, min_samples_leaf=2, n_estimators=1.........
[CV 3/5; 211/400] END max_depth=6, min_samples_leaf=2, n_estimators=1;, score=0.782 total time=   0.0s
[CV 4/5; 211/400] START max_depth=6, min_samples_leaf=2, n_estimators=1.........
[CV 4/5; 211/400] END max_depth=6, min_samples_leaf=2, n_estimators=1;, score=0.768 total time=   0.0s
[CV 5/5; 211/400] START max_depth=6, min_samples_leaf=2, n_estimators=1.........
[CV 5/5; 211/400] END max_depth=6, min_samples_leaf=2, n_estimators=1;, score=0.805 total time=   0.0s
[CV 1/5; 212/400] START max_depth=6, min_samples_leaf=2, n_estimators=2.........
[CV 1/5; 212/400] END max_depth=6, min_samples_leaf=2, n_estimators=2;, score=0.810 total time=   0.0s
[CV 2/5; 212/400] START max_depth=6, min_samples_leaf=2, n_estimators=2.........
[CV 2/5; 212/400] END max_depth=6, min_samples_leaf=2, n_estimators=2;, score=0.796 total time=   0.0s
[CV 3/5; 212/400] START max_depth=6, min_samples_leaf=2, n_estimators=2.........
[CV 3/5; 212/400] END max_depth=6, min_samples_leaf=2, n_estimators=2;, score=0.810 total time=   0.0s
[CV 4/5; 212/400] START max_depth=6, min_samples_leaf=2, n_estimators=2.........
[CV 4/5; 212/400] END max_depth=6, min_samples_leaf=2, n_estimators=2;, score=0.794 total time=   0.0s
[CV 5/5; 212/400] START max_depth=6, min_samples_leaf=2, n_estimators=2.........
[CV 5/5; 212/400] END max_depth=6, min_samples_leaf=2, n_estimators=2;, score=0.799 total time=   0.0s
[CV 1/5; 213/400] START max_depth=6, min_samples_leaf=2, n_estimators=3.........
[CV 1/5; 213/400] END max_depth=6, min_samples_leaf=2, n_estimators=3;, score=0.813 total time=   0.0s
[CV 2/5; 213/400] START max_depth=6, min_samples_leaf=2, n_estimators=3.........
[CV 2/5; 213/400] END max_depth=6, min_samples_leaf=2, n_estimators=3;, score=0.818 total time=   0.0s
[CV 3/5; 213/400] START max_depth=6, min_samples_leaf=2, n_estimators=3.........
[CV 3/5; 213/400] END max_depth=6, min_samples_leaf=2, n_estimators=3;, score=0.819 total time=   0.0s
[CV 4/5; 213/400] START max_depth=6, min_samples_leaf=2, n_estimators=3.........
[CV 4/5; 213/400] END max_depth=6, min_samples_leaf=2, n_estimators=3;, score=0.810 total time=   0.0s
[CV 5/5; 213/400] START max_depth=6, min_samples_leaf=2, n_estimators=3.........
[CV 5/5; 213/400] END max_depth=6, min_samples_leaf=2, n_estimators=3;, score=0.806 total time=   0.0s
[CV 1/5; 214/400] START max_depth=6, min_samples_leaf=2, n_estimators=4.........
[CV 1/5; 214/400] END max_depth=6, min_samples_leaf=2, n_estimators=4;, score=0.814 total time=   0.0s
[CV 2/5; 214/400] START max_depth=6, min_samples_leaf=2, n_estimators=4.........
[CV 2/5; 214/400] END max_depth=6, min_samples_leaf=2, n_estimators=4;, score=0.821 total time=   0.0s
[CV 3/5; 214/400] START max_depth=6, min_samples_leaf=2, n_estimators=4.........
[CV 3/5; 214/400] END max_depth=6, min_samples_leaf=2, n_estimators=4;, score=0.814 total time=   0.0s
[CV 4/5; 214/400] START max_depth=6, min_samples_leaf=2, n_estimators=4.........
[CV 4/5; 214/400] END max_depth=6, min_samples_leaf=2, n_estimators=4;, score=0.816 total time=   0.0s
[CV 5/5; 214/400] START max_depth=6, min_samples_leaf=2, n_estimators=4.........
[CV 5/5; 214/400] END max_depth=6, min_samples_leaf=2, n_estimators=4;, score=0.808 total time=   0.0s
[CV 1/5; 215/400] START max_depth=6, min_samples_leaf=2, n_estimators=5.........
[CV 1/5; 215/400] END max_depth=6, min_samples_leaf=2, n_estimators=5;, score=0.816 total time=   0.0s
[CV 2/5; 215/400] START max_depth=6, min_samples_leaf=2, n_estimators=5.........
[CV 2/5; 215/400] END max_depth=6, min_samples_leaf=2, n_estimators=5;, score=0.813 total time=   0.0s
[CV 3/5; 215/400] START max_depth=6, min_samples_leaf=2, n_estimators=5.........
[CV 3/5; 215/400] END max_depth=6, min_samples_leaf=2, n_estimators=5;, score=0.816 total time=   0.0s
[CV 4/5; 215/400] START max_depth=6, min_samples_leaf=2, n_estimators=5.........
[CV 4/5; 215/400] END max_depth=6, min_samples_leaf=2, n_estimators=5;, score=0.819 total time=   0.0s
[CV 5/5; 215/400] START max_depth=6, min_samples_leaf=2, n_estimators=5.........
[CV 5/5; 215/400] END max_depth=6, min_samples_leaf=2, n_estimators=5;, score=0.801 total time=   0.0s
[CV 1/5; 216/400] START max_depth=6, min_samples_leaf=2, n_estimators=6.........
[CV 1/5; 216/400] END max_depth=6, min_samples_leaf=2, n_estimators=6;, score=0.816 total time=   0.0s
[CV 2/5; 216/400] START max_depth=6, min_samples_leaf=2, n_estimators=6.........
[CV 2/5; 216/400] END max_depth=6, min_samples_leaf=2, n_estimators=6;, score=0.818 total time=   0.0s
[CV 3/5; 216/400] START max_depth=6, min_samples_leaf=2, n_estimators=6.........
[CV 3/5; 216/400] END max_depth=6, min_samples_leaf=2, n_estimators=6;, score=0.799 total time=   0.0s
[CV 4/5; 216/400] START max_depth=6, min_samples_leaf=2, n_estimators=6.........
[CV 4/5; 216/400] END max_depth=6, min_samples_leaf=2, n_estimators=6;, score=0.819 total time=   0.0s
[CV 5/5; 216/400] START max_depth=6, min_samples_leaf=2, n_estimators=6.........
[CV 5/5; 216/400] END max_depth=6, min_samples_leaf=2, n_estimators=6;, score=0.824 total time=   0.0s
[CV 1/5; 217/400] START max_depth=6, min_samples_leaf=2, n_estimators=7.........
[CV 1/5; 217/400] END max_depth=6, min_samples_leaf=2, n_estimators=7;, score=0.824 total time=   0.1s
[CV 2/5; 217/400] START max_depth=6, min_samples_leaf=2, n_estimators=7.........
[CV 2/5; 217/400] END max_depth=6, min_samples_leaf=2, n_estimators=7;, score=0.808 total time=   0.0s
[CV 3/5; 217/400] START max_depth=6, min_samples_leaf=2, n_estimators=7.........
[CV 3/5; 217/400] END max_depth=6, min_samples_leaf=2, n_estimators=7;, score=0.817 total time=   0.0s
[CV 4/5; 217/400] START max_depth=6, min_samples_leaf=2, n_estimators=7.........
[CV 4/5; 217/400] END max_depth=6, min_samples_leaf=2, n_estimators=7;, score=0.811 total time=   0.0s
[CV 5/5; 217/400] START max_depth=6, min_samples_leaf=2, n_estimators=7.........
[CV 5/5; 217/400] END max_depth=6, min_samples_leaf=2, n_estimators=7;, score=0.813 total time=   0.0s
[CV 1/5; 218/400] START max_depth=6, min_samples_leaf=2, n_estimators=8.........
[CV 1/5; 218/400] END max_depth=6, min_samples_leaf=2, n_estimators=8;, score=0.812 total time=   0.0s
[CV 2/5; 218/400] START max_depth=6, min_samples_leaf=2, n_estimators=8.........
[CV 2/5; 218/400] END max_depth=6, min_samples_leaf=2, n_estimators=8;, score=0.828 total time=   0.0s
[CV 3/5; 218/400] START max_depth=6, min_samples_leaf=2, n_estimators=8.........
[CV 3/5; 218/400] END max_depth=6, min_samples_leaf=2, n_estimators=8;, score=0.832 total time=   0.1s
[CV 4/5; 218/400] START max_depth=6, min_samples_leaf=2, n_estimators=8.........
[CV 4/5; 218/400] END max_depth=6, min_samples_leaf=2, n_estimators=8;, score=0.824 total time=   0.1s
[CV 5/5; 218/400] START max_depth=6, min_samples_leaf=2, n_estimators=8.........
[CV 5/5; 218/400] END max_depth=6, min_samples_leaf=2, n_estimators=8;, score=0.803 total time=   0.0s
[CV 1/5; 219/400] START max_depth=6, min_samples_leaf=2, n_estimators=9.........
[CV 1/5; 219/400] END max_depth=6, min_samples_leaf=2, n_estimators=9;, score=0.819 total time=   0.1s
[CV 2/5; 219/400] START max_depth=6, min_samples_leaf=2, n_estimators=9.........
[CV 2/5; 219/400] END max_depth=6, min_samples_leaf=2, n_estimators=9;, score=0.822 total time=   0.1s
[CV 3/5; 219/400] START max_depth=6, min_samples_leaf=2, n_estimators=9.........
[CV 3/5; 219/400] END max_depth=6, min_samples_leaf=2, n_estimators=9;, score=0.807 total time=   0.1s
[CV 4/5; 219/400] START max_depth=6, min_samples_leaf=2, n_estimators=9.........
[CV 4/5; 219/400] END max_depth=6, min_samples_leaf=2, n_estimators=9;, score=0.814 total time=   0.1s
[CV 5/5; 219/400] START max_depth=6, min_samples_leaf=2, n_estimators=9.........
[CV 5/5; 219/400] END max_depth=6, min_samples_leaf=2, n_estimators=9;, score=0.805 total time=   0.1s
[CV 1/5; 220/400] START max_depth=6, min_samples_leaf=2, n_estimators=10........
[CV 1/5; 220/400] END max_depth=6, min_samples_leaf=2, n_estimators=10;, score=0.814 total time=   0.1s
[CV 2/5; 220/400] START max_depth=6, min_samples_leaf=2, n_estimators=10........
[CV 2/5; 220/400] END max_depth=6, min_samples_leaf=2, n_estimators=10;, score=0.807 total time=   0.1s
[CV 3/5; 220/400] START max_depth=6, min_samples_leaf=2, n_estimators=10........
[CV 3/5; 220/400] END max_depth=6, min_samples_leaf=2, n_estimators=10;, score=0.816 total time=   0.1s
[CV 4/5; 220/400] START max_depth=6, min_samples_leaf=2, n_estimators=10........
[CV 4/5; 220/400] END max_depth=6, min_samples_leaf=2, n_estimators=10;, score=0.816 total time=   0.1s
[CV 5/5; 220/400] START max_depth=6, min_samples_leaf=2, n_estimators=10........
[CV 5/5; 220/400] END max_depth=6, min_samples_leaf=2, n_estimators=10;, score=0.802 total time=   0.1s
[CV 1/5; 221/400] START max_depth=6, min_samples_leaf=3, n_estimators=1.........
[CV 1/5; 221/400] END max_depth=6, min_samples_leaf=3, n_estimators=1;, score=0.800 total time=   0.0s
[CV 2/5; 221/400] START max_depth=6, min_samples_leaf=3, n_estimators=1.........
[CV 2/5; 221/400] END max_depth=6, min_samples_leaf=3, n_estimators=1;, score=0.793 total time=   0.0s
[CV 3/5; 221/400] START max_depth=6, min_samples_leaf=3, n_estimators=1.........
[CV 3/5; 221/400] END max_depth=6, min_samples_leaf=3, n_estimators=1;, score=0.785 total time=   0.0s
[CV 4/5; 221/400] START max_depth=6, min_samples_leaf=3, n_estimators=1.........
[CV 4/5; 221/400] END max_depth=6, min_samples_leaf=3, n_estimators=1;, score=0.783 total time=   0.0s
[CV 5/5; 221/400] START max_depth=6, min_samples_leaf=3, n_estimators=1.........
[CV 5/5; 221/400] END max_depth=6, min_samples_leaf=3, n_estimators=1;, score=0.772 total time=   0.0s
[CV 1/5; 222/400] START max_depth=6, min_samples_leaf=3, n_estimators=2.........
[CV 1/5; 222/400] END max_depth=6, min_samples_leaf=3, n_estimators=2;, score=0.815 total time=   0.0s
[CV 2/5; 222/400] START max_depth=6, min_samples_leaf=3, n_estimators=2.........
[CV 2/5; 222/400] END max_depth=6, min_samples_leaf=3, n_estimators=2;, score=0.815 total time=   0.0s
[CV 3/5; 222/400] START max_depth=6, min_samples_leaf=3, n_estimators=2.........
[CV 3/5; 222/400] END max_depth=6, min_samples_leaf=3, n_estimators=2;, score=0.811 total time=   0.0s
[CV 4/5; 222/400] START max_depth=6, min_samples_leaf=3, n_estimators=2.........
[CV 4/5; 222/400] END max_depth=6, min_samples_leaf=3, n_estimators=2;, score=0.809 total time=   0.0s
[CV 5/5; 222/400] START max_depth=6, min_samples_leaf=3, n_estimators=2.........
[CV 5/5; 222/400] END max_depth=6, min_samples_leaf=3, n_estimators=2;, score=0.810 total time=   0.0s
[CV 1/5; 223/400] START max_depth=6, min_samples_leaf=3, n_estimators=3.........
[CV 1/5; 223/400] END max_depth=6, min_samples_leaf=3, n_estimators=3;, score=0.808 total time=   0.0s
[CV 2/5; 223/400] START max_depth=6, min_samples_leaf=3, n_estimators=3.........
[CV 2/5; 223/400] END max_depth=6, min_samples_leaf=3, n_estimators=3;, score=0.812 total time=   0.0s
[CV 3/5; 223/400] START max_depth=6, min_samples_leaf=3, n_estimators=3.........
[CV 3/5; 223/400] END max_depth=6, min_samples_leaf=3, n_estimators=3;, score=0.795 total time=   0.0s
[CV 4/5; 223/400] START max_depth=6, min_samples_leaf=3, n_estimators=3.........
[CV 4/5; 223/400] END max_depth=6, min_samples_leaf=3, n_estimators=3;, score=0.810 total time=   0.0s
[CV 5/5; 223/400] START max_depth=6, min_samples_leaf=3, n_estimators=3.........
[CV 5/5; 223/400] END max_depth=6, min_samples_leaf=3, n_estimators=3;, score=0.816 total time=   0.0s
[CV 1/5; 224/400] START max_depth=6, min_samples_leaf=3, n_estimators=4.........
[CV 1/5; 224/400] END max_depth=6, min_samples_leaf=3, n_estimators=4;, score=0.819 total time=   0.0s
[CV 2/5; 224/400] START max_depth=6, min_samples_leaf=3, n_estimators=4.........
[CV 2/5; 224/400] END max_depth=6, min_samples_leaf=3, n_estimators=4;, score=0.802 total time=   0.0s
[CV 3/5; 224/400] START max_depth=6, min_samples_leaf=3, n_estimators=4.........
[CV 3/5; 224/400] END max_depth=6, min_samples_leaf=3, n_estimators=4;, score=0.816 total time=   0.0s
[CV 4/5; 224/400] START max_depth=6, min_samples_leaf=3, n_estimators=4.........
[CV 4/5; 224/400] END max_depth=6, min_samples_leaf=3, n_estimators=4;, score=0.808 total time=   0.0s
[CV 5/5; 224/400] START max_depth=6, min_samples_leaf=3, n_estimators=4.........
[CV 5/5; 224/400] END max_depth=6, min_samples_leaf=3, n_estimators=4;, score=0.805 total time=   0.0s
[CV 1/5; 225/400] START max_depth=6, min_samples_leaf=3, n_estimators=5.........
[CV 1/5; 225/400] END max_depth=6, min_samples_leaf=3, n_estimators=5;, score=0.816 total time=   0.0s
[CV 2/5; 225/400] START max_depth=6, min_samples_leaf=3, n_estimators=5.........
[CV 2/5; 225/400] END max_depth=6, min_samples_leaf=3, n_estimators=5;, score=0.825 total time=   0.0s
[CV 3/5; 225/400] START max_depth=6, min_samples_leaf=3, n_estimators=5.........
[CV 3/5; 225/400] END max_depth=6, min_samples_leaf=3, n_estimators=5;, score=0.812 total time=   0.0s
[CV 4/5; 225/400] START max_depth=6, min_samples_leaf=3, n_estimators=5.........
[CV 4/5; 225/400] END max_depth=6, min_samples_leaf=3, n_estimators=5;, score=0.816 total time=   0.0s
[CV 5/5; 225/400] START max_depth=6, min_samples_leaf=3, n_estimators=5.........
[CV 5/5; 225/400] END max_depth=6, min_samples_leaf=3, n_estimators=5;, score=0.801 total time=   0.0s
[CV 1/5; 226/400] START max_depth=6, min_samples_leaf=3, n_estimators=6.........
[CV 1/5; 226/400] END max_depth=6, min_samples_leaf=3, n_estimators=6;, score=0.821 total time=   0.0s
[CV 2/5; 226/400] START max_depth=6, min_samples_leaf=3, n_estimators=6.........
[CV 2/5; 226/400] END max_depth=6, min_samples_leaf=3, n_estimators=6;, score=0.817 total time=   0.0s
[CV 3/5; 226/400] START max_depth=6, min_samples_leaf=3, n_estimators=6.........
[CV 3/5; 226/400] END max_depth=6, min_samples_leaf=3, n_estimators=6;, score=0.830 total time=   0.0s
[CV 4/5; 226/400] START max_depth=6, min_samples_leaf=3, n_estimators=6.........
[CV 4/5; 226/400] END max_depth=6, min_samples_leaf=3, n_estimators=6;, score=0.810 total time=   0.0s
[CV 5/5; 226/400] START max_depth=6, min_samples_leaf=3, n_estimators=6.........
[CV 5/5; 226/400] END max_depth=6, min_samples_leaf=3, n_estimators=6;, score=0.802 total time=   0.0s
[CV 1/5; 227/400] START max_depth=6, min_samples_leaf=3, n_estimators=7.........
[CV 1/5; 227/400] END max_depth=6, min_samples_leaf=3, n_estimators=7;, score=0.816 total time=   0.0s
[CV 2/5; 227/400] START max_depth=6, min_samples_leaf=3, n_estimators=7.........
[CV 2/5; 227/400] END max_depth=6, min_samples_leaf=3, n_estimators=7;, score=0.824 total time=   0.1s
[CV 3/5; 227/400] START max_depth=6, min_samples_leaf=3, n_estimators=7.........
[CV 3/5; 227/400] END max_depth=6, min_samples_leaf=3, n_estimators=7;, score=0.813 total time=   0.1s
[CV 4/5; 227/400] START max_depth=6, min_samples_leaf=3, n_estimators=7.........
[CV 4/5; 227/400] END max_depth=6, min_samples_leaf=3, n_estimators=7;, score=0.820 total time=   0.0s
[CV 5/5; 227/400] START max_depth=6, min_samples_leaf=3, n_estimators=7.........
[CV 5/5; 227/400] END max_depth=6, min_samples_leaf=3, n_estimators=7;, score=0.793 total time=   0.1s
[CV 1/5; 228/400] START max_depth=6, min_samples_leaf=3, n_estimators=8.........
[CV 1/5; 228/400] END max_depth=6, min_samples_leaf=3, n_estimators=8;, score=0.828 total time=   0.1s
[CV 2/5; 228/400] START max_depth=6, min_samples_leaf=3, n_estimators=8.........
[CV 2/5; 228/400] END max_depth=6, min_samples_leaf=3, n_estimators=8;, score=0.813 total time=   0.1s
[CV 3/5; 228/400] START max_depth=6, min_samples_leaf=3, n_estimators=8.........
[CV 3/5; 228/400] END max_depth=6, min_samples_leaf=3, n_estimators=8;, score=0.813 total time=   0.1s
[CV 4/5; 228/400] START max_depth=6, min_samples_leaf=3, n_estimators=8.........
[CV 4/5; 228/400] END max_depth=6, min_samples_leaf=3, n_estimators=8;, score=0.813 total time=   0.1s
[CV 5/5; 228/400] START max_depth=6, min_samples_leaf=3, n_estimators=8.........
[CV 5/5; 228/400] END max_depth=6, min_samples_leaf=3, n_estimators=8;, score=0.808 total time=   0.1s
[CV 1/5; 229/400] START max_depth=6, min_samples_leaf=3, n_estimators=9.........
[CV 1/5; 229/400] END max_depth=6, min_samples_leaf=3, n_estimators=9;, score=0.820 total time=   0.1s
[CV 2/5; 229/400] START max_depth=6, min_samples_leaf=3, n_estimators=9.........
[CV 2/5; 229/400] END max_depth=6, min_samples_leaf=3, n_estimators=9;, score=0.821 total time=   0.1s
[CV 3/5; 229/400] START max_depth=6, min_samples_leaf=3, n_estimators=9.........
[CV 3/5; 229/400] END max_depth=6, min_samples_leaf=3, n_estimators=9;, score=0.821 total time=   0.1s
[CV 4/5; 229/400] START max_depth=6, min_samples_leaf=3, n_estimators=9.........
[CV 4/5; 229/400] END max_depth=6, min_samples_leaf=3, n_estimators=9;, score=0.806 total time=   0.1s
[CV 5/5; 229/400] START max_depth=6, min_samples_leaf=3, n_estimators=9.........
[CV 5/5; 229/400] END max_depth=6, min_samples_leaf=3, n_estimators=9;, score=0.810 total time=   0.1s
[CV 1/5; 230/400] START max_depth=6, min_samples_leaf=3, n_estimators=10........
[CV 1/5; 230/400] END max_depth=6, min_samples_leaf=3, n_estimators=10;, score=0.832 total time=   0.1s
[CV 2/5; 230/400] START max_depth=6, min_samples_leaf=3, n_estimators=10........
[CV 2/5; 230/400] END max_depth=6, min_samples_leaf=3, n_estimators=10;, score=0.825 total time=   0.1s
[CV 3/5; 230/400] START max_depth=6, min_samples_leaf=3, n_estimators=10........
[CV 3/5; 230/400] END max_depth=6, min_samples_leaf=3, n_estimators=10;, score=0.816 total time=   0.1s
[CV 4/5; 230/400] START max_depth=6, min_samples_leaf=3, n_estimators=10........
[CV 4/5; 230/400] END max_depth=6, min_samples_leaf=3, n_estimators=10;, score=0.807 total time=   0.1s
[CV 5/5; 230/400] START max_depth=6, min_samples_leaf=3, n_estimators=10........
[CV 5/5; 230/400] END max_depth=6, min_samples_leaf=3, n_estimators=10;, score=0.809 total time=   0.1s
[CV 1/5; 231/400] START max_depth=6, min_samples_leaf=4, n_estimators=1.........
[CV 1/5; 231/400] END max_depth=6, min_samples_leaf=4, n_estimators=1;, score=0.735 total time=   0.0s
[CV 2/5; 231/400] START max_depth=6, min_samples_leaf=4, n_estimators=1.........
[CV 2/5; 231/400] END max_depth=6, min_samples_leaf=4, n_estimators=1;, score=0.790 total time=   0.0s
[CV 3/5; 231/400] START max_depth=6, min_samples_leaf=4, n_estimators=1.........
[CV 3/5; 231/400] END max_depth=6, min_samples_leaf=4, n_estimators=1;, score=0.772 total time=   0.0s
[CV 4/5; 231/400] START max_depth=6, min_samples_leaf=4, n_estimators=1.........
[CV 4/5; 231/400] END max_depth=6, min_samples_leaf=4, n_estimators=1;, score=0.812 total time=   0.0s
[CV 5/5; 231/400] START max_depth=6, min_samples_leaf=4, n_estimators=1.........
[CV 5/5; 231/400] END max_depth=6, min_samples_leaf=4, n_estimators=1;, score=0.775 total time=   0.0s
[CV 1/5; 232/400] START max_depth=6, min_samples_leaf=4, n_estimators=2.........
[CV 1/5; 232/400] END max_depth=6, min_samples_leaf=4, n_estimators=2;, score=0.811 total time=   0.0s
[CV 2/5; 232/400] START max_depth=6, min_samples_leaf=4, n_estimators=2.........
[CV 2/5; 232/400] END max_depth=6, min_samples_leaf=4, n_estimators=2;, score=0.805 total time=   0.0s
[CV 3/5; 232/400] START max_depth=6, min_samples_leaf=4, n_estimators=2.........
[CV 3/5; 232/400] END max_depth=6, min_samples_leaf=4, n_estimators=2;, score=0.809 total time=   0.0s
[CV 4/5; 232/400] START max_depth=6, min_samples_leaf=4, n_estimators=2.........
[CV 4/5; 232/400] END max_depth=6, min_samples_leaf=4, n_estimators=2;, score=0.793 total time=   0.0s
[CV 5/5; 232/400] START max_depth=6, min_samples_leaf=4, n_estimators=2.........
[CV 5/5; 232/400] END max_depth=6, min_samples_leaf=4, n_estimators=2;, score=0.797 total time=   0.0s
[CV 1/5; 233/400] START max_depth=6, min_samples_leaf=4, n_estimators=3.........
[CV 1/5; 233/400] END max_depth=6, min_samples_leaf=4, n_estimators=3;, score=0.819 total time=   0.0s
[CV 2/5; 233/400] START max_depth=6, min_samples_leaf=4, n_estimators=3.........
[CV 2/5; 233/400] END max_depth=6, min_samples_leaf=4, n_estimators=3;, score=0.830 total time=   0.0s
[CV 3/5; 233/400] START max_depth=6, min_samples_leaf=4, n_estimators=3.........
[CV 3/5; 233/400] END max_depth=6, min_samples_leaf=4, n_estimators=3;, score=0.806 total time=   0.0s
[CV 4/5; 233/400] START max_depth=6, min_samples_leaf=4, n_estimators=3.........
[CV 4/5; 233/400] END max_depth=6, min_samples_leaf=4, n_estimators=3;, score=0.823 total time=   0.0s
[CV 5/5; 233/400] START max_depth=6, min_samples_leaf=4, n_estimators=3.........
[CV 5/5; 233/400] END max_depth=6, min_samples_leaf=4, n_estimators=3;, score=0.800 total time=   0.0s
[CV 1/5; 234/400] START max_depth=6, min_samples_leaf=4, n_estimators=4.........
[CV 1/5; 234/400] END max_depth=6, min_samples_leaf=4, n_estimators=4;, score=0.805 total time=   0.0s
[CV 2/5; 234/400] START max_depth=6, min_samples_leaf=4, n_estimators=4.........
[CV 2/5; 234/400] END max_depth=6, min_samples_leaf=4, n_estimators=4;, score=0.810 total time=   0.0s
[CV 3/5; 234/400] START max_depth=6, min_samples_leaf=4, n_estimators=4.........
[CV 3/5; 234/400] END max_depth=6, min_samples_leaf=4, n_estimators=4;, score=0.810 total time=   0.0s
[CV 4/5; 234/400] START max_depth=6, min_samples_leaf=4, n_estimators=4.........
[CV 4/5; 234/400] END max_depth=6, min_samples_leaf=4, n_estimators=4;, score=0.808 total time=   0.0s
[CV 5/5; 234/400] START max_depth=6, min_samples_leaf=4, n_estimators=4.........
[CV 5/5; 234/400] END max_depth=6, min_samples_leaf=4, n_estimators=4;, score=0.808 total time=   0.0s
[CV 1/5; 235/400] START max_depth=6, min_samples_leaf=4, n_estimators=5.........
[CV 1/5; 235/400] END max_depth=6, min_samples_leaf=4, n_estimators=5;, score=0.816 total time=   0.0s
[CV 2/5; 235/400] START max_depth=6, min_samples_leaf=4, n_estimators=5.........
[CV 2/5; 235/400] END max_depth=6, min_samples_leaf=4, n_estimators=5;, score=0.819 total time=   0.0s
[CV 3/5; 235/400] START max_depth=6, min_samples_leaf=4, n_estimators=5.........
[CV 3/5; 235/400] END max_depth=6, min_samples_leaf=4, n_estimators=5;, score=0.817 total time=   0.0s
[CV 4/5; 235/400] START max_depth=6, min_samples_leaf=4, n_estimators=5.........
[CV 4/5; 235/400] END max_depth=6, min_samples_leaf=4, n_estimators=5;, score=0.822 total time=   0.0s
[CV 5/5; 235/400] START max_depth=6, min_samples_leaf=4, n_estimators=5.........
[CV 5/5; 235/400] END max_depth=6, min_samples_leaf=4, n_estimators=5;, score=0.799 total time=   0.0s
[CV 1/5; 236/400] START max_depth=6, min_samples_leaf=4, n_estimators=6.........
[CV 1/5; 236/400] END max_depth=6, min_samples_leaf=4, n_estimators=6;, score=0.817 total time=   0.0s
[CV 2/5; 236/400] START max_depth=6, min_samples_leaf=4, n_estimators=6.........
[CV 2/5; 236/400] END max_depth=6, min_samples_leaf=4, n_estimators=6;, score=0.820 total time=   0.0s
[CV 3/5; 236/400] START max_depth=6, min_samples_leaf=4, n_estimators=6.........
[CV 3/5; 236/400] END max_depth=6, min_samples_leaf=4, n_estimators=6;, score=0.806 total time=   0.0s
[CV 4/5; 236/400] START max_depth=6, min_samples_leaf=4, n_estimators=6.........
[CV 4/5; 236/400] END max_depth=6, min_samples_leaf=4, n_estimators=6;, score=0.812 total time=   0.0s
[CV 5/5; 236/400] START max_depth=6, min_samples_leaf=4, n_estimators=6.........
[CV 5/5; 236/400] END max_depth=6, min_samples_leaf=4, n_estimators=6;, score=0.805 total time=   0.0s
[CV 1/5; 237/400] START max_depth=6, min_samples_leaf=4, n_estimators=7.........
[CV 1/5; 237/400] END max_depth=6, min_samples_leaf=4, n_estimators=7;, score=0.829 total time=   0.0s
[CV 2/5; 237/400] START max_depth=6, min_samples_leaf=4, n_estimators=7.........
[CV 2/5; 237/400] END max_depth=6, min_samples_leaf=4, n_estimators=7;, score=0.828 total time=   0.0s
[CV 3/5; 237/400] START max_depth=6, min_samples_leaf=4, n_estimators=7.........
[CV 3/5; 237/400] END max_depth=6, min_samples_leaf=4, n_estimators=7;, score=0.807 total time=   0.0s
[CV 4/5; 237/400] START max_depth=6, min_samples_leaf=4, n_estimators=7.........
[CV 4/5; 237/400] END max_depth=6, min_samples_leaf=4, n_estimators=7;, score=0.810 total time=   0.0s
[CV 5/5; 237/400] START max_depth=6, min_samples_leaf=4, n_estimators=7.........
[CV 5/5; 237/400] END max_depth=6, min_samples_leaf=4, n_estimators=7;, score=0.810 total time=   0.0s
[CV 1/5; 238/400] START max_depth=6, min_samples_leaf=4, n_estimators=8.........
[CV 1/5; 238/400] END max_depth=6, min_samples_leaf=4, n_estimators=8;, score=0.822 total time=   0.1s
[CV 2/5; 238/400] START max_depth=6, min_samples_leaf=4, n_estimators=8.........
[CV 2/5; 238/400] END max_depth=6, min_samples_leaf=4, n_estimators=8;, score=0.835 total time=   0.1s
[CV 3/5; 238/400] START max_depth=6, min_samples_leaf=4, n_estimators=8.........
[CV 3/5; 238/400] END max_depth=6, min_samples_leaf=4, n_estimators=8;, score=0.821 total time=   0.0s
[CV 4/5; 238/400] START max_depth=6, min_samples_leaf=4, n_estimators=8.........
[CV 4/5; 238/400] END max_depth=6, min_samples_leaf=4, n_estimators=8;, score=0.812 total time=   0.0s
[CV 5/5; 238/400] START max_depth=6, min_samples_leaf=4, n_estimators=8.........
[CV 5/5; 238/400] END max_depth=6, min_samples_leaf=4, n_estimators=8;, score=0.802 total time=   0.0s
[CV 1/5; 239/400] START max_depth=6, min_samples_leaf=4, n_estimators=9.........
[CV 1/5; 239/400] END max_depth=6, min_samples_leaf=4, n_estimators=9;, score=0.817 total time=   0.1s
[CV 2/5; 239/400] START max_depth=6, min_samples_leaf=4, n_estimators=9.........
[CV 2/5; 239/400] END max_depth=6, min_samples_leaf=4, n_estimators=9;, score=0.824 total time=   0.1s
[CV 3/5; 239/400] START max_depth=6, min_samples_leaf=4, n_estimators=9.........
[CV 3/5; 239/400] END max_depth=6, min_samples_leaf=4, n_estimators=9;, score=0.805 total time=   0.1s
[CV 4/5; 239/400] START max_depth=6, min_samples_leaf=4, n_estimators=9.........
[CV 4/5; 239/400] END max_depth=6, min_samples_leaf=4, n_estimators=9;, score=0.816 total time=   0.1s
[CV 5/5; 239/400] START max_depth=6, min_samples_leaf=4, n_estimators=9.........
[CV 5/5; 239/400] END max_depth=6, min_samples_leaf=4, n_estimators=9;, score=0.805 total time=   0.1s
[CV 1/5; 240/400] START max_depth=6, min_samples_leaf=4, n_estimators=10........
[CV 1/5; 240/400] END max_depth=6, min_samples_leaf=4, n_estimators=10;, score=0.818 total time=   0.1s
[CV 2/5; 240/400] START max_depth=6, min_samples_leaf=4, n_estimators=10........
[CV 2/5; 240/400] END max_depth=6, min_samples_leaf=4, n_estimators=10;, score=0.819 total time=   0.1s
[CV 3/5; 240/400] START max_depth=6, min_samples_leaf=4, n_estimators=10........
[CV 3/5; 240/400] END max_depth=6, min_samples_leaf=4, n_estimators=10;, score=0.807 total time=   0.1s
[CV 4/5; 240/400] START max_depth=6, min_samples_leaf=4, n_estimators=10........
[CV 4/5; 240/400] END max_depth=6, min_samples_leaf=4, n_estimators=10;, score=0.807 total time=   0.1s
[CV 5/5; 240/400] START max_depth=6, min_samples_leaf=4, n_estimators=10........
[CV 5/5; 240/400] END max_depth=6, min_samples_leaf=4, n_estimators=10;, score=0.810 total time=   0.1s
[CV 1/5; 241/400] START max_depth=7, min_samples_leaf=1, n_estimators=1.........
[CV 1/5; 241/400] END max_depth=7, min_samples_leaf=1, n_estimators=1;, score=0.784 total time=   0.0s
[CV 2/5; 241/400] START max_depth=7, min_samples_leaf=1, n_estimators=1.........
[CV 2/5; 241/400] END max_depth=7, min_samples_leaf=1, n_estimators=1;, score=0.817 total time=   0.0s
[CV 3/5; 241/400] START max_depth=7, min_samples_leaf=1, n_estimators=1.........
[CV 3/5; 241/400] END max_depth=7, min_samples_leaf=1, n_estimators=1;, score=0.752 total time=   0.0s
[CV 4/5; 241/400] START max_depth=7, min_samples_leaf=1, n_estimators=1.........
[CV 4/5; 241/400] END max_depth=7, min_samples_leaf=1, n_estimators=1;, score=0.804 total time=   0.0s
[CV 5/5; 241/400] START max_depth=7, min_samples_leaf=1, n_estimators=1.........
[CV 5/5; 241/400] END max_depth=7, min_samples_leaf=1, n_estimators=1;, score=0.796 total time=   0.0s
[CV 1/5; 242/400] START max_depth=7, min_samples_leaf=1, n_estimators=2.........
[CV 1/5; 242/400] END max_depth=7, min_samples_leaf=1, n_estimators=2;, score=0.821 total time=   0.0s
[CV 2/5; 242/400] START max_depth=7, min_samples_leaf=1, n_estimators=2.........
[CV 2/5; 242/400] END max_depth=7, min_samples_leaf=1, n_estimators=2;, score=0.825 total time=   0.0s
[CV 3/5; 242/400] START max_depth=7, min_samples_leaf=1, n_estimators=2.........
[CV 3/5; 242/400] END max_depth=7, min_samples_leaf=1, n_estimators=2;, score=0.809 total time=   0.0s
[CV 4/5; 242/400] START max_depth=7, min_samples_leaf=1, n_estimators=2.........
[CV 4/5; 242/400] END max_depth=7, min_samples_leaf=1, n_estimators=2;, score=0.826 total time=   0.0s
[CV 5/5; 242/400] START max_depth=7, min_samples_leaf=1, n_estimators=2.........
[CV 5/5; 242/400] END max_depth=7, min_samples_leaf=1, n_estimators=2;, score=0.799 total time=   0.0s
[CV 1/5; 243/400] START max_depth=7, min_samples_leaf=1, n_estimators=3.........
[CV 1/5; 243/400] END max_depth=7, min_samples_leaf=1, n_estimators=3;, score=0.821 total time=   0.0s
[CV 2/5; 243/400] START max_depth=7, min_samples_leaf=1, n_estimators=3.........
[CV 2/5; 243/400] END max_depth=7, min_samples_leaf=1, n_estimators=3;, score=0.814 total time=   0.0s
[CV 3/5; 243/400] START max_depth=7, min_samples_leaf=1, n_estimators=3.........
[CV 3/5; 243/400] END max_depth=7, min_samples_leaf=1, n_estimators=3;, score=0.837 total time=   0.0s
[CV 4/5; 243/400] START max_depth=7, min_samples_leaf=1, n_estimators=3.........
[CV 4/5; 243/400] END max_depth=7, min_samples_leaf=1, n_estimators=3;, score=0.832 total time=   0.0s
[CV 5/5; 243/400] START max_depth=7, min_samples_leaf=1, n_estimators=3.........
[CV 5/5; 243/400] END max_depth=7, min_samples_leaf=1, n_estimators=3;, score=0.803 total time=   0.0s
[CV 1/5; 244/400] START max_depth=7, min_samples_leaf=1, n_estimators=4.........
[CV 1/5; 244/400] END max_depth=7, min_samples_leaf=1, n_estimators=4;, score=0.828 total time=   0.0s
[CV 2/5; 244/400] START max_depth=7, min_samples_leaf=1, n_estimators=4.........
[CV 2/5; 244/400] END max_depth=7, min_samples_leaf=1, n_estimators=4;, score=0.825 total time=   0.0s
[CV 3/5; 244/400] START max_depth=7, min_samples_leaf=1, n_estimators=4.........
[CV 3/5; 244/400] END max_depth=7, min_samples_leaf=1, n_estimators=4;, score=0.816 total time=   0.0s
[CV 4/5; 244/400] START max_depth=7, min_samples_leaf=1, n_estimators=4.........
[CV 4/5; 244/400] END max_depth=7, min_samples_leaf=1, n_estimators=4;, score=0.820 total time=   0.0s
[CV 5/5; 244/400] START max_depth=7, min_samples_leaf=1, n_estimators=4.........
[CV 5/5; 244/400] END max_depth=7, min_samples_leaf=1, n_estimators=4;, score=0.820 total time=   0.0s
[CV 1/5; 245/400] START max_depth=7, min_samples_leaf=1, n_estimators=5.........
[CV 1/5; 245/400] END max_depth=7, min_samples_leaf=1, n_estimators=5;, score=0.828 total time=   0.0s
[CV 2/5; 245/400] START max_depth=7, min_samples_leaf=1, n_estimators=5.........
[CV 2/5; 245/400] END max_depth=7, min_samples_leaf=1, n_estimators=5;, score=0.842 total time=   0.0s
[CV 3/5; 245/400] START max_depth=7, min_samples_leaf=1, n_estimators=5.........
[CV 3/5; 245/400] END max_depth=7, min_samples_leaf=1, n_estimators=5;, score=0.824 total time=   0.0s
[CV 4/5; 245/400] START max_depth=7, min_samples_leaf=1, n_estimators=5.........
[CV 4/5; 245/400] END max_depth=7, min_samples_leaf=1, n_estimators=5;, score=0.838 total time=   0.0s
[CV 5/5; 245/400] START max_depth=7, min_samples_leaf=1, n_estimators=5.........
[CV 5/5; 245/400] END max_depth=7, min_samples_leaf=1, n_estimators=5;, score=0.829 total time=   0.0s
[CV 1/5; 246/400] START max_depth=7, min_samples_leaf=1, n_estimators=6.........
[CV 1/5; 246/400] END max_depth=7, min_samples_leaf=1, n_estimators=6;, score=0.836 total time=   0.1s
[CV 2/5; 246/400] START max_depth=7, min_samples_leaf=1, n_estimators=6.........
[CV 2/5; 246/400] END max_depth=7, min_samples_leaf=1, n_estimators=6;, score=0.843 total time=   0.1s
[CV 3/5; 246/400] START max_depth=7, min_samples_leaf=1, n_estimators=6.........
[CV 3/5; 246/400] END max_depth=7, min_samples_leaf=1, n_estimators=6;, score=0.829 total time=   0.1s
[CV 4/5; 246/400] START max_depth=7, min_samples_leaf=1, n_estimators=6.........
[CV 4/5; 246/400] END max_depth=7, min_samples_leaf=1, n_estimators=6;, score=0.819 total time=   0.1s
[CV 5/5; 246/400] START max_depth=7, min_samples_leaf=1, n_estimators=6.........
[CV 5/5; 246/400] END max_depth=7, min_samples_leaf=1, n_estimators=6;, score=0.828 total time=   0.1s
[CV 1/5; 247/400] START max_depth=7, min_samples_leaf=1, n_estimators=7.........
[CV 1/5; 247/400] END max_depth=7, min_samples_leaf=1, n_estimators=7;, score=0.832 total time=   0.1s
[CV 2/5; 247/400] START max_depth=7, min_samples_leaf=1, n_estimators=7.........
[CV 2/5; 247/400] END max_depth=7, min_samples_leaf=1, n_estimators=7;, score=0.835 total time=   0.1s
[CV 3/5; 247/400] START max_depth=7, min_samples_leaf=1, n_estimators=7.........
[CV 3/5; 247/400] END max_depth=7, min_samples_leaf=1, n_estimators=7;, score=0.828 total time=   0.1s
[CV 4/5; 247/400] START max_depth=7, min_samples_leaf=1, n_estimators=7.........
[CV 4/5; 247/400] END max_depth=7, min_samples_leaf=1, n_estimators=7;, score=0.840 total time=   0.1s
[CV 5/5; 247/400] START max_depth=7, min_samples_leaf=1, n_estimators=7.........
[CV 5/5; 247/400] END max_depth=7, min_samples_leaf=1, n_estimators=7;, score=0.816 total time=   0.1s
[CV 1/5; 248/400] START max_depth=7, min_samples_leaf=1, n_estimators=8.........
[CV 1/5; 248/400] END max_depth=7, min_samples_leaf=1, n_estimators=8;, score=0.838 total time=   0.1s
[CV 2/5; 248/400] START max_depth=7, min_samples_leaf=1, n_estimators=8.........
[CV 2/5; 248/400] END max_depth=7, min_samples_leaf=1, n_estimators=8;, score=0.837 total time=   0.1s
[CV 3/5; 248/400] START max_depth=7, min_samples_leaf=1, n_estimators=8.........
[CV 3/5; 248/400] END max_depth=7, min_samples_leaf=1, n_estimators=8;, score=0.822 total time=   0.1s
[CV 4/5; 248/400] START max_depth=7, min_samples_leaf=1, n_estimators=8.........
[CV 4/5; 248/400] END max_depth=7, min_samples_leaf=1, n_estimators=8;, score=0.836 total time=   0.1s
[CV 5/5; 248/400] START max_depth=7, min_samples_leaf=1, n_estimators=8.........
[CV 5/5; 248/400] END max_depth=7, min_samples_leaf=1, n_estimators=8;, score=0.832 total time=   0.1s
[CV 1/5; 249/400] START max_depth=7, min_samples_leaf=1, n_estimators=9.........
[CV 1/5; 249/400] END max_depth=7, min_samples_leaf=1, n_estimators=9;, score=0.830 total time=   0.1s
[CV 2/5; 249/400] START max_depth=7, min_samples_leaf=1, n_estimators=9.........
[CV 2/5; 249/400] END max_depth=7, min_samples_leaf=1, n_estimators=9;, score=0.824 total time=   0.1s
[CV 3/5; 249/400] START max_depth=7, min_samples_leaf=1, n_estimators=9.........
[CV 3/5; 249/400] END max_depth=7, min_samples_leaf=1, n_estimators=9;, score=0.823 total time=   0.1s
[CV 4/5; 249/400] START max_depth=7, min_samples_leaf=1, n_estimators=9.........
[CV 4/5; 249/400] END max_depth=7, min_samples_leaf=1, n_estimators=9;, score=0.834 total time=   0.1s
[CV 5/5; 249/400] START max_depth=7, min_samples_leaf=1, n_estimators=9.........
[CV 5/5; 249/400] END max_depth=7, min_samples_leaf=1, n_estimators=9;, score=0.830 total time=   0.1s
[CV 1/5; 250/400] START max_depth=7, min_samples_leaf=1, n_estimators=10........
[CV 1/5; 250/400] END max_depth=7, min_samples_leaf=1, n_estimators=10;, score=0.830 total time=   0.1s
[CV 2/5; 250/400] START max_depth=7, min_samples_leaf=1, n_estimators=10........
[CV 2/5; 250/400] END max_depth=7, min_samples_leaf=1, n_estimators=10;, score=0.826 total time=   0.1s
[CV 3/5; 250/400] START max_depth=7, min_samples_leaf=1, n_estimators=10........
[CV 3/5; 250/400] END max_depth=7, min_samples_leaf=1, n_estimators=10;, score=0.829 total time=   0.1s
[CV 4/5; 250/400] START max_depth=7, min_samples_leaf=1, n_estimators=10........
[CV 4/5; 250/400] END max_depth=7, min_samples_leaf=1, n_estimators=10;, score=0.840 total time=   0.1s
[CV 5/5; 250/400] START max_depth=7, min_samples_leaf=1, n_estimators=10........
[CV 5/5; 250/400] END max_depth=7, min_samples_leaf=1, n_estimators=10;, score=0.834 total time=   0.1s
[CV 1/5; 251/400] START max_depth=7, min_samples_leaf=2, n_estimators=1.........
[CV 1/5; 251/400] END max_depth=7, min_samples_leaf=2, n_estimators=1;, score=0.815 total time=   0.0s
[CV 2/5; 251/400] START max_depth=7, min_samples_leaf=2, n_estimators=1.........
[CV 2/5; 251/400] END max_depth=7, min_samples_leaf=2, n_estimators=1;, score=0.717 total time=   0.0s
[CV 3/5; 251/400] START max_depth=7, min_samples_leaf=2, n_estimators=1.........
[CV 3/5; 251/400] END max_depth=7, min_samples_leaf=2, n_estimators=1;, score=0.801 total time=   0.0s
[CV 4/5; 251/400] START max_depth=7, min_samples_leaf=2, n_estimators=1.........
[CV 4/5; 251/400] END max_depth=7, min_samples_leaf=2, n_estimators=1;, score=0.791 total time=   0.0s
[CV 5/5; 251/400] START max_depth=7, min_samples_leaf=2, n_estimators=1.........
[CV 5/5; 251/400] END max_depth=7, min_samples_leaf=2, n_estimators=1;, score=0.788 total time=   0.0s
[CV 1/5; 252/400] START max_depth=7, min_samples_leaf=2, n_estimators=2.........
[CV 1/5; 252/400] END max_depth=7, min_samples_leaf=2, n_estimators=2;, score=0.821 total time=   0.0s
[CV 2/5; 252/400] START max_depth=7, min_samples_leaf=2, n_estimators=2.........
[CV 2/5; 252/400] END max_depth=7, min_samples_leaf=2, n_estimators=2;, score=0.812 total time=   0.0s
[CV 3/5; 252/400] START max_depth=7, min_samples_leaf=2, n_estimators=2.........
[CV 3/5; 252/400] END max_depth=7, min_samples_leaf=2, n_estimators=2;, score=0.796 total time=   0.0s
[CV 4/5; 252/400] START max_depth=7, min_samples_leaf=2, n_estimators=2.........
[CV 4/5; 252/400] END max_depth=7, min_samples_leaf=2, n_estimators=2;, score=0.832 total time=   0.0s
[CV 5/5; 252/400] START max_depth=7, min_samples_leaf=2, n_estimators=2.........
[CV 5/5; 252/400] END max_depth=7, min_samples_leaf=2, n_estimators=2;, score=0.790 total time=   0.0s
[CV 1/5; 253/400] START max_depth=7, min_samples_leaf=2, n_estimators=3.........
[CV 1/5; 253/400] END max_depth=7, min_samples_leaf=2, n_estimators=3;, score=0.830 total time=   0.0s
[CV 2/5; 253/400] START max_depth=7, min_samples_leaf=2, n_estimators=3.........
[CV 2/5; 253/400] END max_depth=7, min_samples_leaf=2, n_estimators=3;, score=0.821 total time=   0.0s
[CV 3/5; 253/400] START max_depth=7, min_samples_leaf=2, n_estimators=3.........
[CV 3/5; 253/400] END max_depth=7, min_samples_leaf=2, n_estimators=3;, score=0.814 total time=   0.0s
[CV 4/5; 253/400] START max_depth=7, min_samples_leaf=2, n_estimators=3.........
[CV 4/5; 253/400] END max_depth=7, min_samples_leaf=2, n_estimators=3;, score=0.806 total time=   0.0s
[CV 5/5; 253/400] START max_depth=7, min_samples_leaf=2, n_estimators=3.........
[CV 5/5; 253/400] END max_depth=7, min_samples_leaf=2, n_estimators=3;, score=0.814 total time=   0.0s
[CV 1/5; 254/400] START max_depth=7, min_samples_leaf=2, n_estimators=4.........
[CV 1/5; 254/400] END max_depth=7, min_samples_leaf=2, n_estimators=4;, score=0.825 total time=   0.0s
[CV 2/5; 254/400] START max_depth=7, min_samples_leaf=2, n_estimators=4.........
[CV 2/5; 254/400] END max_depth=7, min_samples_leaf=2, n_estimators=4;, score=0.818 total time=   0.0s
[CV 3/5; 254/400] START max_depth=7, min_samples_leaf=2, n_estimators=4.........
[CV 3/5; 254/400] END max_depth=7, min_samples_leaf=2, n_estimators=4;, score=0.826 total time=   0.0s
[CV 4/5; 254/400] START max_depth=7, min_samples_leaf=2, n_estimators=4.........
[CV 4/5; 254/400] END max_depth=7, min_samples_leaf=2, n_estimators=4;, score=0.824 total time=   0.0s
[CV 5/5; 254/400] START max_depth=7, min_samples_leaf=2, n_estimators=4.........
[CV 5/5; 254/400] END max_depth=7, min_samples_leaf=2, n_estimators=4;, score=0.809 total time=   0.0s
[CV 1/5; 255/400] START max_depth=7, min_samples_leaf=2, n_estimators=5.........
[CV 1/5; 255/400] END max_depth=7, min_samples_leaf=2, n_estimators=5;, score=0.829 total time=   0.0s
[CV 2/5; 255/400] START max_depth=7, min_samples_leaf=2, n_estimators=5.........
[CV 2/5; 255/400] END max_depth=7, min_samples_leaf=2, n_estimators=5;, score=0.834 total time=   0.0s
[CV 3/5; 255/400] START max_depth=7, min_samples_leaf=2, n_estimators=5.........
[CV 3/5; 255/400] END max_depth=7, min_samples_leaf=2, n_estimators=5;, score=0.823 total time=   0.0s
[CV 4/5; 255/400] START max_depth=7, min_samples_leaf=2, n_estimators=5.........
[CV 4/5; 255/400] END max_depth=7, min_samples_leaf=2, n_estimators=5;, score=0.834 total time=   0.0s
[CV 5/5; 255/400] START max_depth=7, min_samples_leaf=2, n_estimators=5.........
[CV 5/5; 255/400] END max_depth=7, min_samples_leaf=2, n_estimators=5;, score=0.812 total time=   0.0s
[CV 1/5; 256/400] START max_depth=7, min_samples_leaf=2, n_estimators=6.........
[CV 1/5; 256/400] END max_depth=7, min_samples_leaf=2, n_estimators=6;, score=0.832 total time=   0.0s
[CV 2/5; 256/400] START max_depth=7, min_samples_leaf=2, n_estimators=6.........
[CV 2/5; 256/400] END max_depth=7, min_samples_leaf=2, n_estimators=6;, score=0.838 total time=   0.0s
[CV 3/5; 256/400] START max_depth=7, min_samples_leaf=2, n_estimators=6.........
[CV 3/5; 256/400] END max_depth=7, min_samples_leaf=2, n_estimators=6;, score=0.823 total time=   0.0s
[CV 4/5; 256/400] START max_depth=7, min_samples_leaf=2, n_estimators=6.........
[CV 4/5; 256/400] END max_depth=7, min_samples_leaf=2, n_estimators=6;, score=0.837 total time=   0.0s
[CV 5/5; 256/400] START max_depth=7, min_samples_leaf=2, n_estimators=6.........
[CV 5/5; 256/400] END max_depth=7, min_samples_leaf=2, n_estimators=6;, score=0.813 total time=   0.0s
[CV 1/5; 257/400] START max_depth=7, min_samples_leaf=2, n_estimators=7.........
[CV 1/5; 257/400] END max_depth=7, min_samples_leaf=2, n_estimators=7;, score=0.828 total time=   0.1s
[CV 2/5; 257/400] START max_depth=7, min_samples_leaf=2, n_estimators=7.........
[CV 2/5; 257/400] END max_depth=7, min_samples_leaf=2, n_estimators=7;, score=0.827 total time=   0.0s
[CV 3/5; 257/400] START max_depth=7, min_samples_leaf=2, n_estimators=7.........
[CV 3/5; 257/400] END max_depth=7, min_samples_leaf=2, n_estimators=7;, score=0.830 total time=   0.1s
[CV 4/5; 257/400] START max_depth=7, min_samples_leaf=2, n_estimators=7.........
[CV 4/5; 257/400] END max_depth=7, min_samples_leaf=2, n_estimators=7;, score=0.838 total time=   0.0s
[CV 5/5; 257/400] START max_depth=7, min_samples_leaf=2, n_estimators=7.........
[CV 5/5; 257/400] END max_depth=7, min_samples_leaf=2, n_estimators=7;, score=0.827 total time=   0.0s
[CV 1/5; 258/400] START max_depth=7, min_samples_leaf=2, n_estimators=8.........
[CV 1/5; 258/400] END max_depth=7, min_samples_leaf=2, n_estimators=8;, score=0.832 total time=   0.1s
[CV 2/5; 258/400] START max_depth=7, min_samples_leaf=2, n_estimators=8.........
[CV 2/5; 258/400] END max_depth=7, min_samples_leaf=2, n_estimators=8;, score=0.828 total time=   0.1s
[CV 3/5; 258/400] START max_depth=7, min_samples_leaf=2, n_estimators=8.........
[CV 3/5; 258/400] END max_depth=7, min_samples_leaf=2, n_estimators=8;, score=0.830 total time=   0.1s
[CV 4/5; 258/400] START max_depth=7, min_samples_leaf=2, n_estimators=8.........
[CV 4/5; 258/400] END max_depth=7, min_samples_leaf=2, n_estimators=8;, score=0.838 total time=   0.1s
[CV 5/5; 258/400] START max_depth=7, min_samples_leaf=2, n_estimators=8.........
[CV 5/5; 258/400] END max_depth=7, min_samples_leaf=2, n_estimators=8;, score=0.832 total time=   0.1s
[CV 1/5; 259/400] START max_depth=7, min_samples_leaf=2, n_estimators=9.........
[CV 1/5; 259/400] END max_depth=7, min_samples_leaf=2, n_estimators=9;, score=0.826 total time=   0.1s
[CV 2/5; 259/400] START max_depth=7, min_samples_leaf=2, n_estimators=9.........
[CV 2/5; 259/400] END max_depth=7, min_samples_leaf=2, n_estimators=9;, score=0.841 total time=   0.1s
[CV 3/5; 259/400] START max_depth=7, min_samples_leaf=2, n_estimators=9.........
[CV 3/5; 259/400] END max_depth=7, min_samples_leaf=2, n_estimators=9;, score=0.841 total time=   0.1s
[CV 4/5; 259/400] START max_depth=7, min_samples_leaf=2, n_estimators=9.........
[CV 4/5; 259/400] END max_depth=7, min_samples_leaf=2, n_estimators=9;, score=0.837 total time=   0.1s
[CV 5/5; 259/400] START max_depth=7, min_samples_leaf=2, n_estimators=9.........
[CV 5/5; 259/400] END max_depth=7, min_samples_leaf=2, n_estimators=9;, score=0.832 total time=   0.1s
[CV 1/5; 260/400] START max_depth=7, min_samples_leaf=2, n_estimators=10........
[CV 1/5; 260/400] END max_depth=7, min_samples_leaf=2, n_estimators=10;, score=0.835 total time=   0.1s
[CV 2/5; 260/400] START max_depth=7, min_samples_leaf=2, n_estimators=10........
[CV 2/5; 260/400] END max_depth=7, min_samples_leaf=2, n_estimators=10;, score=0.841 total time=   0.1s
[CV 3/5; 260/400] START max_depth=7, min_samples_leaf=2, n_estimators=10........
[CV 3/5; 260/400] END max_depth=7, min_samples_leaf=2, n_estimators=10;, score=0.827 total time=   0.1s
[CV 4/5; 260/400] START max_depth=7, min_samples_leaf=2, n_estimators=10........
[CV 4/5; 260/400] END max_depth=7, min_samples_leaf=2, n_estimators=10;, score=0.835 total time=   0.1s
[CV 5/5; 260/400] START max_depth=7, min_samples_leaf=2, n_estimators=10........
[CV 5/5; 260/400] END max_depth=7, min_samples_leaf=2, n_estimators=10;, score=0.827 total time=   0.1s
[CV 1/5; 261/400] START max_depth=7, min_samples_leaf=3, n_estimators=1.........
[CV 1/5; 261/400] END max_depth=7, min_samples_leaf=3, n_estimators=1;, score=0.815 total time=   0.0s
[CV 2/5; 261/400] START max_depth=7, min_samples_leaf=3, n_estimators=1.........
[CV 2/5; 261/400] END max_depth=7, min_samples_leaf=3, n_estimators=1;, score=0.820 total time=   0.0s
[CV 3/5; 261/400] START max_depth=7, min_samples_leaf=3, n_estimators=1.........
[CV 3/5; 261/400] END max_depth=7, min_samples_leaf=3, n_estimators=1;, score=0.782 total time=   0.0s
[CV 4/5; 261/400] START max_depth=7, min_samples_leaf=3, n_estimators=1.........
[CV 4/5; 261/400] END max_depth=7, min_samples_leaf=3, n_estimators=1;, score=0.814 total time=   0.0s
[CV 5/5; 261/400] START max_depth=7, min_samples_leaf=3, n_estimators=1.........
[CV 5/5; 261/400] END max_depth=7, min_samples_leaf=3, n_estimators=1;, score=0.798 total time=   0.0s
[CV 1/5; 262/400] START max_depth=7, min_samples_leaf=3, n_estimators=2.........
[CV 1/5; 262/400] END max_depth=7, min_samples_leaf=3, n_estimators=2;, score=0.822 total time=   0.0s
[CV 2/5; 262/400] START max_depth=7, min_samples_leaf=3, n_estimators=2.........
[CV 2/5; 262/400] END max_depth=7, min_samples_leaf=3, n_estimators=2;, score=0.820 total time=   0.0s
[CV 3/5; 262/400] START max_depth=7, min_samples_leaf=3, n_estimators=2.........
[CV 3/5; 262/400] END max_depth=7, min_samples_leaf=3, n_estimators=2;, score=0.783 total time=   0.0s
[CV 4/5; 262/400] START max_depth=7, min_samples_leaf=3, n_estimators=2.........
[CV 4/5; 262/400] END max_depth=7, min_samples_leaf=3, n_estimators=2;, score=0.833 total time=   0.0s
[CV 5/5; 262/400] START max_depth=7, min_samples_leaf=3, n_estimators=2.........
[CV 5/5; 262/400] END max_depth=7, min_samples_leaf=3, n_estimators=2;, score=0.819 total time=   0.0s
[CV 1/5; 263/400] START max_depth=7, min_samples_leaf=3, n_estimators=3.........
[CV 1/5; 263/400] END max_depth=7, min_samples_leaf=3, n_estimators=3;, score=0.821 total time=   0.0s
[CV 2/5; 263/400] START max_depth=7, min_samples_leaf=3, n_estimators=3.........
[CV 2/5; 263/400] END max_depth=7, min_samples_leaf=3, n_estimators=3;, score=0.816 total time=   0.0s
[CV 3/5; 263/400] START max_depth=7, min_samples_leaf=3, n_estimators=3.........
[CV 3/5; 263/400] END max_depth=7, min_samples_leaf=3, n_estimators=3;, score=0.820 total time=   0.0s
[CV 4/5; 263/400] START max_depth=7, min_samples_leaf=3, n_estimators=3.........
[CV 4/5; 263/400] END max_depth=7, min_samples_leaf=3, n_estimators=3;, score=0.821 total time=   0.0s
[CV 5/5; 263/400] START max_depth=7, min_samples_leaf=3, n_estimators=3.........
[CV 5/5; 263/400] END max_depth=7, min_samples_leaf=3, n_estimators=3;, score=0.828 total time=   0.0s
[CV 1/5; 264/400] START max_depth=7, min_samples_leaf=3, n_estimators=4.........
[CV 1/5; 264/400] END max_depth=7, min_samples_leaf=3, n_estimators=4;, score=0.838 total time=   0.0s
[CV 2/5; 264/400] START max_depth=7, min_samples_leaf=3, n_estimators=4.........
[CV 2/5; 264/400] END max_depth=7, min_samples_leaf=3, n_estimators=4;, score=0.850 total time=   0.0s
[CV 3/5; 264/400] START max_depth=7, min_samples_leaf=3, n_estimators=4.........
[CV 3/5; 264/400] END max_depth=7, min_samples_leaf=3, n_estimators=4;, score=0.830 total time=   0.0s
[CV 4/5; 264/400] START max_depth=7, min_samples_leaf=3, n_estimators=4.........
[CV 4/5; 264/400] END max_depth=7, min_samples_leaf=3, n_estimators=4;, score=0.813 total time=   0.0s
[CV 5/5; 264/400] START max_depth=7, min_samples_leaf=3, n_estimators=4.........
[CV 5/5; 264/400] END max_depth=7, min_samples_leaf=3, n_estimators=4;, score=0.819 total time=   0.0s
[CV 1/5; 265/400] START max_depth=7, min_samples_leaf=3, n_estimators=5.........
[CV 1/5; 265/400] END max_depth=7, min_samples_leaf=3, n_estimators=5;, score=0.846 total time=   0.0s
[CV 2/5; 265/400] START max_depth=7, min_samples_leaf=3, n_estimators=5.........
[CV 2/5; 265/400] END max_depth=7, min_samples_leaf=3, n_estimators=5;, score=0.821 total time=   0.0s
[CV 3/5; 265/400] START max_depth=7, min_samples_leaf=3, n_estimators=5.........
[CV 3/5; 265/400] END max_depth=7, min_samples_leaf=3, n_estimators=5;, score=0.821 total time=   0.0s
[CV 4/5; 265/400] START max_depth=7, min_samples_leaf=3, n_estimators=5.........
[CV 4/5; 265/400] END max_depth=7, min_samples_leaf=3, n_estimators=5;, score=0.834 total time=   0.0s
[CV 5/5; 265/400] START max_depth=7, min_samples_leaf=3, n_estimators=5.........
[CV 5/5; 265/400] END max_depth=7, min_samples_leaf=3, n_estimators=5;, score=0.813 total time=   0.0s
[CV 1/5; 266/400] START max_depth=7, min_samples_leaf=3, n_estimators=6.........
[CV 1/5; 266/400] END max_depth=7, min_samples_leaf=3, n_estimators=6;, score=0.834 total time=   0.0s
[CV 2/5; 266/400] START max_depth=7, min_samples_leaf=3, n_estimators=6.........
[CV 2/5; 266/400] END max_depth=7, min_samples_leaf=3, n_estimators=6;, score=0.823 total time=   0.0s
[CV 3/5; 266/400] START max_depth=7, min_samples_leaf=3, n_estimators=6.........
[CV 3/5; 266/400] END max_depth=7, min_samples_leaf=3, n_estimators=6;, score=0.825 total time=   0.0s
[CV 4/5; 266/400] START max_depth=7, min_samples_leaf=3, n_estimators=6.........
[CV 4/5; 266/400] END max_depth=7, min_samples_leaf=3, n_estimators=6;, score=0.835 total time=   0.0s
[CV 5/5; 266/400] START max_depth=7, min_samples_leaf=3, n_estimators=6.........
[CV 5/5; 266/400] END max_depth=7, min_samples_leaf=3, n_estimators=6;, score=0.813 total time=   0.0s
[CV 1/5; 267/400] START max_depth=7, min_samples_leaf=3, n_estimators=7.........
[CV 1/5; 267/400] END max_depth=7, min_samples_leaf=3, n_estimators=7;, score=0.831 total time=   0.0s
[CV 2/5; 267/400] START max_depth=7, min_samples_leaf=3, n_estimators=7.........
[CV 2/5; 267/400] END max_depth=7, min_samples_leaf=3, n_estimators=7;, score=0.833 total time=   0.0s
[CV 3/5; 267/400] START max_depth=7, min_samples_leaf=3, n_estimators=7.........
[CV 3/5; 267/400] END max_depth=7, min_samples_leaf=3, n_estimators=7;, score=0.821 total time=   0.1s
[CV 4/5; 267/400] START max_depth=7, min_samples_leaf=3, n_estimators=7.........
[CV 4/5; 267/400] END max_depth=7, min_samples_leaf=3, n_estimators=7;, score=0.827 total time=   0.0s
[CV 5/5; 267/400] START max_depth=7, min_samples_leaf=3, n_estimators=7.........
[CV 5/5; 267/400] END max_depth=7, min_samples_leaf=3, n_estimators=7;, score=0.826 total time=   0.0s
[CV 1/5; 268/400] START max_depth=7, min_samples_leaf=3, n_estimators=8.........
[CV 1/5; 268/400] END max_depth=7, min_samples_leaf=3, n_estimators=8;, score=0.852 total time=   0.1s
[CV 2/5; 268/400] START max_depth=7, min_samples_leaf=3, n_estimators=8.........
[CV 2/5; 268/400] END max_depth=7, min_samples_leaf=3, n_estimators=8;, score=0.838 total time=   0.0s
[CV 3/5; 268/400] START max_depth=7, min_samples_leaf=3, n_estimators=8.........
[CV 3/5; 268/400] END max_depth=7, min_samples_leaf=3, n_estimators=8;, score=0.821 total time=   0.1s
[CV 4/5; 268/400] START max_depth=7, min_samples_leaf=3, n_estimators=8.........
[CV 4/5; 268/400] END max_depth=7, min_samples_leaf=3, n_estimators=8;, score=0.830 total time=   0.0s
[CV 5/5; 268/400] START max_depth=7, min_samples_leaf=3, n_estimators=8.........
[CV 5/5; 268/400] END max_depth=7, min_samples_leaf=3, n_estimators=8;, score=0.820 total time=   0.1s
[CV 1/5; 269/400] START max_depth=7, min_samples_leaf=3, n_estimators=9.........
[CV 1/5; 269/400] END max_depth=7, min_samples_leaf=3, n_estimators=9;, score=0.832 total time=   0.1s
[CV 2/5; 269/400] START max_depth=7, min_samples_leaf=3, n_estimators=9.........
[CV 2/5; 269/400] END max_depth=7, min_samples_leaf=3, n_estimators=9;, score=0.815 total time=   0.1s
[CV 3/5; 269/400] START max_depth=7, min_samples_leaf=3, n_estimators=9.........
[CV 3/5; 269/400] END max_depth=7, min_samples_leaf=3, n_estimators=9;, score=0.821 total time=   0.1s
[CV 4/5; 269/400] START max_depth=7, min_samples_leaf=3, n_estimators=9.........
[CV 4/5; 269/400] END max_depth=7, min_samples_leaf=3, n_estimators=9;, score=0.836 total time=   0.1s
[CV 5/5; 269/400] START max_depth=7, min_samples_leaf=3, n_estimators=9.........
[CV 5/5; 269/400] END max_depth=7, min_samples_leaf=3, n_estimators=9;, score=0.819 total time=   0.1s
[CV 1/5; 270/400] START max_depth=7, min_samples_leaf=3, n_estimators=10........
[CV 1/5; 270/400] END max_depth=7, min_samples_leaf=3, n_estimators=10;, score=0.830 total time=   0.1s
[CV 2/5; 270/400] START max_depth=7, min_samples_leaf=3, n_estimators=10........
[CV 2/5; 270/400] END max_depth=7, min_samples_leaf=3, n_estimators=10;, score=0.835 total time=   0.1s
[CV 3/5; 270/400] START max_depth=7, min_samples_leaf=3, n_estimators=10........
[CV 3/5; 270/400] END max_depth=7, min_samples_leaf=3, n_estimators=10;, score=0.827 total time=   0.1s
[CV 4/5; 270/400] START max_depth=7, min_samples_leaf=3, n_estimators=10........
[CV 4/5; 270/400] END max_depth=7, min_samples_leaf=3, n_estimators=10;, score=0.830 total time=   0.1s
[CV 5/5; 270/400] START max_depth=7, min_samples_leaf=3, n_estimators=10........
[CV 5/5; 270/400] END max_depth=7, min_samples_leaf=3, n_estimators=10;, score=0.827 total time=   0.1s
[CV 1/5; 271/400] START max_depth=7, min_samples_leaf=4, n_estimators=1.........
[CV 1/5; 271/400] END max_depth=7, min_samples_leaf=4, n_estimators=1;, score=0.796 total time=   0.0s
[CV 2/5; 271/400] START max_depth=7, min_samples_leaf=4, n_estimators=1.........
[CV 2/5; 271/400] END max_depth=7, min_samples_leaf=4, n_estimators=1;, score=0.816 total time=   0.0s
[CV 3/5; 271/400] START max_depth=7, min_samples_leaf=4, n_estimators=1.........
[CV 3/5; 271/400] END max_depth=7, min_samples_leaf=4, n_estimators=1;, score=0.800 total time=   0.0s
[CV 4/5; 271/400] START max_depth=7, min_samples_leaf=4, n_estimators=1.........
[CV 4/5; 271/400] END max_depth=7, min_samples_leaf=4, n_estimators=1;, score=0.798 total time=   0.0s
[CV 5/5; 271/400] START max_depth=7, min_samples_leaf=4, n_estimators=1.........
[CV 5/5; 271/400] END max_depth=7, min_samples_leaf=4, n_estimators=1;, score=0.755 total time=   0.0s
[CV 1/5; 272/400] START max_depth=7, min_samples_leaf=4, n_estimators=2.........
[CV 1/5; 272/400] END max_depth=7, min_samples_leaf=4, n_estimators=2;, score=0.831 total time=   0.0s
[CV 2/5; 272/400] START max_depth=7, min_samples_leaf=4, n_estimators=2.........
[CV 2/5; 272/400] END max_depth=7, min_samples_leaf=4, n_estimators=2;, score=0.816 total time=   0.0s
[CV 3/5; 272/400] START max_depth=7, min_samples_leaf=4, n_estimators=2.........
[CV 3/5; 272/400] END max_depth=7, min_samples_leaf=4, n_estimators=2;, score=0.829 total time=   0.0s
[CV 4/5; 272/400] START max_depth=7, min_samples_leaf=4, n_estimators=2.........
[CV 4/5; 272/400] END max_depth=7, min_samples_leaf=4, n_estimators=2;, score=0.816 total time=   0.0s
[CV 5/5; 272/400] START max_depth=7, min_samples_leaf=4, n_estimators=2.........
[CV 5/5; 272/400] END max_depth=7, min_samples_leaf=4, n_estimators=2;, score=0.794 total time=   0.0s
[CV 1/5; 273/400] START max_depth=7, min_samples_leaf=4, n_estimators=3.........
[CV 1/5; 273/400] END max_depth=7, min_samples_leaf=4, n_estimators=3;, score=0.825 total time=   0.0s
[CV 2/5; 273/400] START max_depth=7, min_samples_leaf=4, n_estimators=3.........
[CV 2/5; 273/400] END max_depth=7, min_samples_leaf=4, n_estimators=3;, score=0.819 total time=   0.0s
[CV 3/5; 273/400] START max_depth=7, min_samples_leaf=4, n_estimators=3.........
[CV 3/5; 273/400] END max_depth=7, min_samples_leaf=4, n_estimators=3;, score=0.839 total time=   0.0s
[CV 4/5; 273/400] START max_depth=7, min_samples_leaf=4, n_estimators=3.........
[CV 4/5; 273/400] END max_depth=7, min_samples_leaf=4, n_estimators=3;, score=0.810 total time=   0.0s
[CV 5/5; 273/400] START max_depth=7, min_samples_leaf=4, n_estimators=3.........
[CV 5/5; 273/400] END max_depth=7, min_samples_leaf=4, n_estimators=3;, score=0.819 total time=   0.0s
[CV 1/5; 274/400] START max_depth=7, min_samples_leaf=4, n_estimators=4.........
[CV 1/5; 274/400] END max_depth=7, min_samples_leaf=4, n_estimators=4;, score=0.824 total time=   0.0s
[CV 2/5; 274/400] START max_depth=7, min_samples_leaf=4, n_estimators=4.........
[CV 2/5; 274/400] END max_depth=7, min_samples_leaf=4, n_estimators=4;, score=0.819 total time=   0.0s
[CV 3/5; 274/400] START max_depth=7, min_samples_leaf=4, n_estimators=4.........
[CV 3/5; 274/400] END max_depth=7, min_samples_leaf=4, n_estimators=4;, score=0.816 total time=   0.0s
[CV 4/5; 274/400] START max_depth=7, min_samples_leaf=4, n_estimators=4.........
[CV 4/5; 274/400] END max_depth=7, min_samples_leaf=4, n_estimators=4;, score=0.820 total time=   0.0s
[CV 5/5; 274/400] START max_depth=7, min_samples_leaf=4, n_estimators=4.........
[CV 5/5; 274/400] END max_depth=7, min_samples_leaf=4, n_estimators=4;, score=0.810 total time=   0.0s
[CV 1/5; 275/400] START max_depth=7, min_samples_leaf=4, n_estimators=5.........
[CV 1/5; 275/400] END max_depth=7, min_samples_leaf=4, n_estimators=5;, score=0.836 total time=   0.0s
[CV 2/5; 275/400] START max_depth=7, min_samples_leaf=4, n_estimators=5.........
[CV 2/5; 275/400] END max_depth=7, min_samples_leaf=4, n_estimators=5;, score=0.816 total time=   0.0s
[CV 3/5; 275/400] START max_depth=7, min_samples_leaf=4, n_estimators=5.........
[CV 3/5; 275/400] END max_depth=7, min_samples_leaf=4, n_estimators=5;, score=0.809 total time=   0.0s
[CV 4/5; 275/400] START max_depth=7, min_samples_leaf=4, n_estimators=5.........
[CV 4/5; 275/400] END max_depth=7, min_samples_leaf=4, n_estimators=5;, score=0.831 total time=   0.0s
[CV 5/5; 275/400] START max_depth=7, min_samples_leaf=4, n_estimators=5.........
[CV 5/5; 275/400] END max_depth=7, min_samples_leaf=4, n_estimators=5;, score=0.816 total time=   0.0s
[CV 1/5; 276/400] START max_depth=7, min_samples_leaf=4, n_estimators=6.........
[CV 1/5; 276/400] END max_depth=7, min_samples_leaf=4, n_estimators=6;, score=0.828 total time=   0.0s
[CV 2/5; 276/400] START max_depth=7, min_samples_leaf=4, n_estimators=6.........
[CV 2/5; 276/400] END max_depth=7, min_samples_leaf=4, n_estimators=6;, score=0.813 total time=   0.0s
[CV 3/5; 276/400] START max_depth=7, min_samples_leaf=4, n_estimators=6.........
[CV 3/5; 276/400] END max_depth=7, min_samples_leaf=4, n_estimators=6;, score=0.813 total time=   0.0s
[CV 4/5; 276/400] START max_depth=7, min_samples_leaf=4, n_estimators=6.........
[CV 4/5; 276/400] END max_depth=7, min_samples_leaf=4, n_estimators=6;, score=0.831 total time=   0.1s
[CV 5/5; 276/400] START max_depth=7, min_samples_leaf=4, n_estimators=6.........
[CV 5/5; 276/400] END max_depth=7, min_samples_leaf=4, n_estimators=6;, score=0.825 total time=   0.0s
[CV 1/5; 277/400] START max_depth=7, min_samples_leaf=4, n_estimators=7.........
[CV 1/5; 277/400] END max_depth=7, min_samples_leaf=4, n_estimators=7;, score=0.834 total time=   0.0s
[CV 2/5; 277/400] START max_depth=7, min_samples_leaf=4, n_estimators=7.........
[CV 2/5; 277/400] END max_depth=7, min_samples_leaf=4, n_estimators=7;, score=0.836 total time=   0.0s
[CV 3/5; 277/400] START max_depth=7, min_samples_leaf=4, n_estimators=7.........
[CV 3/5; 277/400] END max_depth=7, min_samples_leaf=4, n_estimators=7;, score=0.829 total time=   0.0s
[CV 4/5; 277/400] START max_depth=7, min_samples_leaf=4, n_estimators=7.........
[CV 4/5; 277/400] END max_depth=7, min_samples_leaf=4, n_estimators=7;, score=0.833 total time=   0.0s
[CV 5/5; 277/400] START max_depth=7, min_samples_leaf=4, n_estimators=7.........
[CV 5/5; 277/400] END max_depth=7, min_samples_leaf=4, n_estimators=7;, score=0.830 total time=   0.0s
[CV 1/5; 278/400] START max_depth=7, min_samples_leaf=4, n_estimators=8.........
[CV 1/5; 278/400] END max_depth=7, min_samples_leaf=4, n_estimators=8;, score=0.830 total time=   0.1s
[CV 2/5; 278/400] START max_depth=7, min_samples_leaf=4, n_estimators=8.........
[CV 2/5; 278/400] END max_depth=7, min_samples_leaf=4, n_estimators=8;, score=0.826 total time=   0.1s
[CV 3/5; 278/400] START max_depth=7, min_samples_leaf=4, n_estimators=8.........
[CV 3/5; 278/400] END max_depth=7, min_samples_leaf=4, n_estimators=8;, score=0.820 total time=   0.0s
[CV 4/5; 278/400] START max_depth=7, min_samples_leaf=4, n_estimators=8.........
[CV 4/5; 278/400] END max_depth=7, min_samples_leaf=4, n_estimators=8;, score=0.824 total time=   0.0s
[CV 5/5; 278/400] START max_depth=7, min_samples_leaf=4, n_estimators=8.........
[CV 5/5; 278/400] END max_depth=7, min_samples_leaf=4, n_estimators=8;, score=0.827 total time=   0.1s
[CV 1/5; 279/400] START max_depth=7, min_samples_leaf=4, n_estimators=9.........
[CV 1/5; 279/400] END max_depth=7, min_samples_leaf=4, n_estimators=9;, score=0.826 total time=   0.1s
[CV 2/5; 279/400] START max_depth=7, min_samples_leaf=4, n_estimators=9.........
[CV 2/5; 279/400] END max_depth=7, min_samples_leaf=4, n_estimators=9;, score=0.819 total time=   0.1s
[CV 3/5; 279/400] START max_depth=7, min_samples_leaf=4, n_estimators=9.........
[CV 3/5; 279/400] END max_depth=7, min_samples_leaf=4, n_estimators=9;, score=0.823 total time=   0.1s
[CV 4/5; 279/400] START max_depth=7, min_samples_leaf=4, n_estimators=9.........
[CV 4/5; 279/400] END max_depth=7, min_samples_leaf=4, n_estimators=9;, score=0.833 total time=   0.1s
[CV 5/5; 279/400] START max_depth=7, min_samples_leaf=4, n_estimators=9.........
[CV 5/5; 279/400] END max_depth=7, min_samples_leaf=4, n_estimators=9;, score=0.822 total time=   0.1s
[CV 1/5; 280/400] START max_depth=7, min_samples_leaf=4, n_estimators=10........
[CV 1/5; 280/400] END max_depth=7, min_samples_leaf=4, n_estimators=10;, score=0.832 total time=   0.1s
[CV 2/5; 280/400] START max_depth=7, min_samples_leaf=4, n_estimators=10........
[CV 2/5; 280/400] END max_depth=7, min_samples_leaf=4, n_estimators=10;, score=0.828 total time=   0.1s
[CV 3/5; 280/400] START max_depth=7, min_samples_leaf=4, n_estimators=10........
[CV 3/5; 280/400] END max_depth=7, min_samples_leaf=4, n_estimators=10;, score=0.835 total time=   0.1s
[CV 4/5; 280/400] START max_depth=7, min_samples_leaf=4, n_estimators=10........
[CV 4/5; 280/400] END max_depth=7, min_samples_leaf=4, n_estimators=10;, score=0.827 total time=   0.1s
[CV 5/5; 280/400] START max_depth=7, min_samples_leaf=4, n_estimators=10........
[CV 5/5; 280/400] END max_depth=7, min_samples_leaf=4, n_estimators=10;, score=0.812 total time=   0.1s
[CV 1/5; 281/400] START max_depth=8, min_samples_leaf=1, n_estimators=1.........
[CV 1/5; 281/400] END max_depth=8, min_samples_leaf=1, n_estimators=1;, score=0.805 total time=   0.0s
[CV 2/5; 281/400] START max_depth=8, min_samples_leaf=1, n_estimators=1.........
[CV 2/5; 281/400] END max_depth=8, min_samples_leaf=1, n_estimators=1;, score=0.819 total time=   0.0s
[CV 3/5; 281/400] START max_depth=8, min_samples_leaf=1, n_estimators=1.........
[CV 3/5; 281/400] END max_depth=8, min_samples_leaf=1, n_estimators=1;, score=0.754 total time=   0.0s
[CV 4/5; 281/400] START max_depth=8, min_samples_leaf=1, n_estimators=1.........
[CV 4/5; 281/400] END max_depth=8, min_samples_leaf=1, n_estimators=1;, score=0.808 total time=   0.0s
[CV 5/5; 281/400] START max_depth=8, min_samples_leaf=1, n_estimators=1.........
[CV 5/5; 281/400] END max_depth=8, min_samples_leaf=1, n_estimators=1;, score=0.802 total time=   0.0s
[CV 1/5; 282/400] START max_depth=8, min_samples_leaf=1, n_estimators=2.........
[CV 1/5; 282/400] END max_depth=8, min_samples_leaf=1, n_estimators=2;, score=0.838 total time=   0.0s
[CV 2/5; 282/400] START max_depth=8, min_samples_leaf=1, n_estimators=2.........
[CV 2/5; 282/400] END max_depth=8, min_samples_leaf=1, n_estimators=2;, score=0.833 total time=   0.0s
[CV 3/5; 282/400] START max_depth=8, min_samples_leaf=1, n_estimators=2.........
[CV 3/5; 282/400] END max_depth=8, min_samples_leaf=1, n_estimators=2;, score=0.833 total time=   0.0s
[CV 4/5; 282/400] START max_depth=8, min_samples_leaf=1, n_estimators=2.........
[CV 4/5; 282/400] END max_depth=8, min_samples_leaf=1, n_estimators=2;, score=0.827 total time=   0.0s
[CV 5/5; 282/400] START max_depth=8, min_samples_leaf=1, n_estimators=2.........
[CV 5/5; 282/400] END max_depth=8, min_samples_leaf=1, n_estimators=2;, score=0.809 total time=   0.0s
[CV 1/5; 283/400] START max_depth=8, min_samples_leaf=1, n_estimators=3.........
[CV 1/5; 283/400] END max_depth=8, min_samples_leaf=1, n_estimators=3;, score=0.846 total time=   0.0s
[CV 2/5; 283/400] START max_depth=8, min_samples_leaf=1, n_estimators=3.........
[CV 2/5; 283/400] END max_depth=8, min_samples_leaf=1, n_estimators=3;, score=0.841 total time=   0.0s
[CV 3/5; 283/400] START max_depth=8, min_samples_leaf=1, n_estimators=3.........
[CV 3/5; 283/400] END max_depth=8, min_samples_leaf=1, n_estimators=3;, score=0.838 total time=   0.0s
[CV 4/5; 283/400] START max_depth=8, min_samples_leaf=1, n_estimators=3.........
[CV 4/5; 283/400] END max_depth=8, min_samples_leaf=1, n_estimators=3;, score=0.835 total time=   0.0s
[CV 5/5; 283/400] START max_depth=8, min_samples_leaf=1, n_estimators=3.........
[CV 5/5; 283/400] END max_depth=8, min_samples_leaf=1, n_estimators=3;, score=0.840 total time=   0.0s
[CV 1/5; 284/400] START max_depth=8, min_samples_leaf=1, n_estimators=4.........
[CV 1/5; 284/400] END max_depth=8, min_samples_leaf=1, n_estimators=4;, score=0.854 total time=   0.0s
[CV 2/5; 284/400] START max_depth=8, min_samples_leaf=1, n_estimators=4.........
[CV 2/5; 284/400] END max_depth=8, min_samples_leaf=1, n_estimators=4;, score=0.843 total time=   0.0s
[CV 3/5; 284/400] START max_depth=8, min_samples_leaf=1, n_estimators=4.........
[CV 3/5; 284/400] END max_depth=8, min_samples_leaf=1, n_estimators=4;, score=0.841 total time=   0.0s
[CV 4/5; 284/400] START max_depth=8, min_samples_leaf=1, n_estimators=4.........
[CV 4/5; 284/400] END max_depth=8, min_samples_leaf=1, n_estimators=4;, score=0.835 total time=   0.0s
[CV 5/5; 284/400] START max_depth=8, min_samples_leaf=1, n_estimators=4.........
[CV 5/5; 284/400] END max_depth=8, min_samples_leaf=1, n_estimators=4;, score=0.827 total time=   0.0s
[CV 1/5; 285/400] START max_depth=8, min_samples_leaf=1, n_estimators=5.........
[CV 1/5; 285/400] END max_depth=8, min_samples_leaf=1, n_estimators=5;, score=0.844 total time=   0.0s
[CV 2/5; 285/400] START max_depth=8, min_samples_leaf=1, n_estimators=5.........
[CV 2/5; 285/400] END max_depth=8, min_samples_leaf=1, n_estimators=5;, score=0.841 total time=   0.0s
[CV 3/5; 285/400] START max_depth=8, min_samples_leaf=1, n_estimators=5.........
[CV 3/5; 285/400] END max_depth=8, min_samples_leaf=1, n_estimators=5;, score=0.826 total time=   0.0s
[CV 4/5; 285/400] START max_depth=8, min_samples_leaf=1, n_estimators=5.........
[CV 4/5; 285/400] END max_depth=8, min_samples_leaf=1, n_estimators=5;, score=0.837 total time=   0.0s
[CV 5/5; 285/400] START max_depth=8, min_samples_leaf=1, n_estimators=5.........
[CV 5/5; 285/400] END max_depth=8, min_samples_leaf=1, n_estimators=5;, score=0.835 total time=   0.0s
[CV 1/5; 286/400] START max_depth=8, min_samples_leaf=1, n_estimators=6.........
[CV 1/5; 286/400] END max_depth=8, min_samples_leaf=1, n_estimators=6;, score=0.832 total time=   0.0s
[CV 2/5; 286/400] START max_depth=8, min_samples_leaf=1, n_estimators=6.........
[CV 2/5; 286/400] END max_depth=8, min_samples_leaf=1, n_estimators=6;, score=0.838 total time=   0.0s
[CV 3/5; 286/400] START max_depth=8, min_samples_leaf=1, n_estimators=6.........
[CV 3/5; 286/400] END max_depth=8, min_samples_leaf=1, n_estimators=6;, score=0.838 total time=   0.0s
[CV 4/5; 286/400] START max_depth=8, min_samples_leaf=1, n_estimators=6.........
[CV 4/5; 286/400] END max_depth=8, min_samples_leaf=1, n_estimators=6;, score=0.844 total time=   0.1s
[CV 5/5; 286/400] START max_depth=8, min_samples_leaf=1, n_estimators=6.........
[CV 5/5; 286/400] END max_depth=8, min_samples_leaf=1, n_estimators=6;, score=0.837 total time=   0.1s
[CV 1/5; 287/400] START max_depth=8, min_samples_leaf=1, n_estimators=7.........
[CV 1/5; 287/400] END max_depth=8, min_samples_leaf=1, n_estimators=7;, score=0.843 total time=   0.1s
[CV 2/5; 287/400] START max_depth=8, min_samples_leaf=1, n_estimators=7.........
[CV 2/5; 287/400] END max_depth=8, min_samples_leaf=1, n_estimators=7;, score=0.844 total time=   0.1s
[CV 3/5; 287/400] START max_depth=8, min_samples_leaf=1, n_estimators=7.........
[CV 3/5; 287/400] END max_depth=8, min_samples_leaf=1, n_estimators=7;, score=0.846 total time=   0.1s
[CV 4/5; 287/400] START max_depth=8, min_samples_leaf=1, n_estimators=7.........
[CV 4/5; 287/400] END max_depth=8, min_samples_leaf=1, n_estimators=7;, score=0.843 total time=   0.0s
[CV 5/5; 287/400] START max_depth=8, min_samples_leaf=1, n_estimators=7.........
[CV 5/5; 287/400] END max_depth=8, min_samples_leaf=1, n_estimators=7;, score=0.849 total time=   0.1s
[CV 1/5; 288/400] START max_depth=8, min_samples_leaf=1, n_estimators=8.........
[CV 1/5; 288/400] END max_depth=8, min_samples_leaf=1, n_estimators=8;, score=0.846 total time=   0.1s
[CV 2/5; 288/400] START max_depth=8, min_samples_leaf=1, n_estimators=8.........
[CV 2/5; 288/400] END max_depth=8, min_samples_leaf=1, n_estimators=8;, score=0.851 total time=   0.1s
[CV 3/5; 288/400] START max_depth=8, min_samples_leaf=1, n_estimators=8.........
[CV 3/5; 288/400] END max_depth=8, min_samples_leaf=1, n_estimators=8;, score=0.846 total time=   0.1s
[CV 4/5; 288/400] START max_depth=8, min_samples_leaf=1, n_estimators=8.........
[CV 4/5; 288/400] END max_depth=8, min_samples_leaf=1, n_estimators=8;, score=0.857 total time=   0.1s
[CV 5/5; 288/400] START max_depth=8, min_samples_leaf=1, n_estimators=8.........
[CV 5/5; 288/400] END max_depth=8, min_samples_leaf=1, n_estimators=8;, score=0.846 total time=   0.1s
[CV 1/5; 289/400] START max_depth=8, min_samples_leaf=1, n_estimators=9.........
[CV 1/5; 289/400] END max_depth=8, min_samples_leaf=1, n_estimators=9;, score=0.852 total time=   0.1s
[CV 2/5; 289/400] START max_depth=8, min_samples_leaf=1, n_estimators=9.........
[CV 2/5; 289/400] END max_depth=8, min_samples_leaf=1, n_estimators=9;, score=0.857 total time=   0.1s
[CV 3/5; 289/400] START max_depth=8, min_samples_leaf=1, n_estimators=9.........
[CV 3/5; 289/400] END max_depth=8, min_samples_leaf=1, n_estimators=9;, score=0.849 total time=   0.1s
[CV 4/5; 289/400] START max_depth=8, min_samples_leaf=1, n_estimators=9.........
[CV 4/5; 289/400] END max_depth=8, min_samples_leaf=1, n_estimators=9;, score=0.845 total time=   0.1s
[CV 5/5; 289/400] START max_depth=8, min_samples_leaf=1, n_estimators=9.........
[CV 5/5; 289/400] END max_depth=8, min_samples_leaf=1, n_estimators=9;, score=0.838 total time=   0.1s
[CV 1/5; 290/400] START max_depth=8, min_samples_leaf=1, n_estimators=10........
[CV 1/5; 290/400] END max_depth=8, min_samples_leaf=1, n_estimators=10;, score=0.860 total time=   0.1s
[CV 2/5; 290/400] START max_depth=8, min_samples_leaf=1, n_estimators=10........
[CV 2/5; 290/400] END max_depth=8, min_samples_leaf=1, n_estimators=10;, score=0.843 total time=   0.1s
[CV 3/5; 290/400] START max_depth=8, min_samples_leaf=1, n_estimators=10........
[CV 3/5; 290/400] END max_depth=8, min_samples_leaf=1, n_estimators=10;, score=0.855 total time=   0.1s
[CV 4/5; 290/400] START max_depth=8, min_samples_leaf=1, n_estimators=10........
[CV 4/5; 290/400] END max_depth=8, min_samples_leaf=1, n_estimators=10;, score=0.843 total time=   0.1s
[CV 5/5; 290/400] START max_depth=8, min_samples_leaf=1, n_estimators=10........
[CV 5/5; 290/400] END max_depth=8, min_samples_leaf=1, n_estimators=10;, score=0.852 total time=   0.1s
[CV 1/5; 291/400] START max_depth=8, min_samples_leaf=2, n_estimators=1.........
[CV 1/5; 291/400] END max_depth=8, min_samples_leaf=2, n_estimators=1;, score=0.780 total time=   0.0s
[CV 2/5; 291/400] START max_depth=8, min_samples_leaf=2, n_estimators=1.........
[CV 2/5; 291/400] END max_depth=8, min_samples_leaf=2, n_estimators=1;, score=0.816 total time=   0.0s
[CV 3/5; 291/400] START max_depth=8, min_samples_leaf=2, n_estimators=1.........
[CV 3/5; 291/400] END max_depth=8, min_samples_leaf=2, n_estimators=1;, score=0.807 total time=   0.0s
[CV 4/5; 291/400] START max_depth=8, min_samples_leaf=2, n_estimators=1.........
[CV 4/5; 291/400] END max_depth=8, min_samples_leaf=2, n_estimators=1;, score=0.824 total time=   0.0s
[CV 5/5; 291/400] START max_depth=8, min_samples_leaf=2, n_estimators=1.........
[CV 5/5; 291/400] END max_depth=8, min_samples_leaf=2, n_estimators=1;, score=0.769 total time=   0.0s
[CV 1/5; 292/400] START max_depth=8, min_samples_leaf=2, n_estimators=2.........
[CV 1/5; 292/400] END max_depth=8, min_samples_leaf=2, n_estimators=2;, score=0.817 total time=   0.0s
[CV 2/5; 292/400] START max_depth=8, min_samples_leaf=2, n_estimators=2.........
[CV 2/5; 292/400] END max_depth=8, min_samples_leaf=2, n_estimators=2;, score=0.847 total time=   0.0s
[CV 3/5; 292/400] START max_depth=8, min_samples_leaf=2, n_estimators=2.........
[CV 3/5; 292/400] END max_depth=8, min_samples_leaf=2, n_estimators=2;, score=0.791 total time=   0.0s
[CV 4/5; 292/400] START max_depth=8, min_samples_leaf=2, n_estimators=2.........
[CV 4/5; 292/400] END max_depth=8, min_samples_leaf=2, n_estimators=2;, score=0.807 total time=   0.0s
[CV 5/5; 292/400] START max_depth=8, min_samples_leaf=2, n_estimators=2.........
[CV 5/5; 292/400] END max_depth=8, min_samples_leaf=2, n_estimators=2;, score=0.833 total time=   0.0s
[CV 1/5; 293/400] START max_depth=8, min_samples_leaf=2, n_estimators=3.........
[CV 1/5; 293/400] END max_depth=8, min_samples_leaf=2, n_estimators=3;, score=0.830 total time=   0.0s
[CV 2/5; 293/400] START max_depth=8, min_samples_leaf=2, n_estimators=3.........
[CV 2/5; 293/400] END max_depth=8, min_samples_leaf=2, n_estimators=3;, score=0.839 total time=   0.0s
[CV 3/5; 293/400] START max_depth=8, min_samples_leaf=2, n_estimators=3.........
[CV 3/5; 293/400] END max_depth=8, min_samples_leaf=2, n_estimators=3;, score=0.829 total time=   0.0s
[CV 4/5; 293/400] START max_depth=8, min_samples_leaf=2, n_estimators=3.........
[CV 4/5; 293/400] END max_depth=8, min_samples_leaf=2, n_estimators=3;, score=0.827 total time=   0.0s
[CV 5/5; 293/400] START max_depth=8, min_samples_leaf=2, n_estimators=3.........
[CV 5/5; 293/400] END max_depth=8, min_samples_leaf=2, n_estimators=3;, score=0.829 total time=   0.0s
[CV 1/5; 294/400] START max_depth=8, min_samples_leaf=2, n_estimators=4.........
[CV 1/5; 294/400] END max_depth=8, min_samples_leaf=2, n_estimators=4;, score=0.835 total time=   0.0s
[CV 2/5; 294/400] START max_depth=8, min_samples_leaf=2, n_estimators=4.........
[CV 2/5; 294/400] END max_depth=8, min_samples_leaf=2, n_estimators=4;, score=0.841 total time=   0.0s
[CV 3/5; 294/400] START max_depth=8, min_samples_leaf=2, n_estimators=4.........
[CV 3/5; 294/400] END max_depth=8, min_samples_leaf=2, n_estimators=4;, score=0.829 total time=   0.0s
[CV 4/5; 294/400] START max_depth=8, min_samples_leaf=2, n_estimators=4.........
[CV 4/5; 294/400] END max_depth=8, min_samples_leaf=2, n_estimators=4;, score=0.838 total time=   0.0s
[CV 5/5; 294/400] START max_depth=8, min_samples_leaf=2, n_estimators=4.........
[CV 5/5; 294/400] END max_depth=8, min_samples_leaf=2, n_estimators=4;, score=0.838 total time=   0.0s
[CV 1/5; 295/400] START max_depth=8, min_samples_leaf=2, n_estimators=5.........
[CV 1/5; 295/400] END max_depth=8, min_samples_leaf=2, n_estimators=5;, score=0.857 total time=   0.0s
[CV 2/5; 295/400] START max_depth=8, min_samples_leaf=2, n_estimators=5.........
[CV 2/5; 295/400] END max_depth=8, min_samples_leaf=2, n_estimators=5;, score=0.849 total time=   0.0s
[CV 3/5; 295/400] START max_depth=8, min_samples_leaf=2, n_estimators=5.........
[CV 3/5; 295/400] END max_depth=8, min_samples_leaf=2, n_estimators=5;, score=0.843 total time=   0.0s
[CV 4/5; 295/400] START max_depth=8, min_samples_leaf=2, n_estimators=5.........
[CV 4/5; 295/400] END max_depth=8, min_samples_leaf=2, n_estimators=5;, score=0.845 total time=   0.0s
[CV 5/5; 295/400] START max_depth=8, min_samples_leaf=2, n_estimators=5.........
[CV 5/5; 295/400] END max_depth=8, min_samples_leaf=2, n_estimators=5;, score=0.832 total time=   0.0s
[CV 1/5; 296/400] START max_depth=8, min_samples_leaf=2, n_estimators=6.........
[CV 1/5; 296/400] END max_depth=8, min_samples_leaf=2, n_estimators=6;, score=0.841 total time=   0.0s
[CV 2/5; 296/400] START max_depth=8, min_samples_leaf=2, n_estimators=6.........
[CV 2/5; 296/400] END max_depth=8, min_samples_leaf=2, n_estimators=6;, score=0.831 total time=   0.0s
[CV 3/5; 296/400] START max_depth=8, min_samples_leaf=2, n_estimators=6.........
[CV 3/5; 296/400] END max_depth=8, min_samples_leaf=2, n_estimators=6;, score=0.846 total time=   0.1s
[CV 4/5; 296/400] START max_depth=8, min_samples_leaf=2, n_estimators=6.........
[CV 4/5; 296/400] END max_depth=8, min_samples_leaf=2, n_estimators=6;, score=0.857 total time=   0.0s
[CV 5/5; 296/400] START max_depth=8, min_samples_leaf=2, n_estimators=6.........
[CV 5/5; 296/400] END max_depth=8, min_samples_leaf=2, n_estimators=6;, score=0.843 total time=   0.0s
[CV 1/5; 297/400] START max_depth=8, min_samples_leaf=2, n_estimators=7.........
[CV 1/5; 297/400] END max_depth=8, min_samples_leaf=2, n_estimators=7;, score=0.841 total time=   0.0s
[CV 2/5; 297/400] START max_depth=8, min_samples_leaf=2, n_estimators=7.........
[CV 2/5; 297/400] END max_depth=8, min_samples_leaf=2, n_estimators=7;, score=0.852 total time=   0.0s
[CV 3/5; 297/400] START max_depth=8, min_samples_leaf=2, n_estimators=7.........
[CV 3/5; 297/400] END max_depth=8, min_samples_leaf=2, n_estimators=7;, score=0.846 total time=   0.0s
[CV 4/5; 297/400] START max_depth=8, min_samples_leaf=2, n_estimators=7.........
[CV 4/5; 297/400] END max_depth=8, min_samples_leaf=2, n_estimators=7;, score=0.851 total time=   0.0s
[CV 5/5; 297/400] START max_depth=8, min_samples_leaf=2, n_estimators=7.........
[CV 5/5; 297/400] END max_depth=8, min_samples_leaf=2, n_estimators=7;, score=0.830 total time=   0.0s
[CV 1/5; 298/400] START max_depth=8, min_samples_leaf=2, n_estimators=8.........
[CV 1/5; 298/400] END max_depth=8, min_samples_leaf=2, n_estimators=8;, score=0.849 total time=   0.1s
[CV 2/5; 298/400] START max_depth=8, min_samples_leaf=2, n_estimators=8.........
[CV 2/5; 298/400] END max_depth=8, min_samples_leaf=2, n_estimators=8;, score=0.851 total time=   0.1s
[CV 3/5; 298/400] START max_depth=8, min_samples_leaf=2, n_estimators=8.........
[CV 3/5; 298/400] END max_depth=8, min_samples_leaf=2, n_estimators=8;, score=0.838 total time=   0.1s
[CV 4/5; 298/400] START max_depth=8, min_samples_leaf=2, n_estimators=8.........
[CV 4/5; 298/400] END max_depth=8, min_samples_leaf=2, n_estimators=8;, score=0.843 total time=   0.1s
[CV 5/5; 298/400] START max_depth=8, min_samples_leaf=2, n_estimators=8.........
[CV 5/5; 298/400] END max_depth=8, min_samples_leaf=2, n_estimators=8;, score=0.834 total time=   0.1s
[CV 1/5; 299/400] START max_depth=8, min_samples_leaf=2, n_estimators=9.........
[CV 1/5; 299/400] END max_depth=8, min_samples_leaf=2, n_estimators=9;, score=0.857 total time=   0.1s
[CV 2/5; 299/400] START max_depth=8, min_samples_leaf=2, n_estimators=9.........
[CV 2/5; 299/400] END max_depth=8, min_samples_leaf=2, n_estimators=9;, score=0.856 total time=   0.1s
[CV 3/5; 299/400] START max_depth=8, min_samples_leaf=2, n_estimators=9.........
[CV 3/5; 299/400] END max_depth=8, min_samples_leaf=2, n_estimators=9;, score=0.849 total time=   0.1s
[CV 4/5; 299/400] START max_depth=8, min_samples_leaf=2, n_estimators=9.........
[CV 4/5; 299/400] END max_depth=8, min_samples_leaf=2, n_estimators=9;, score=0.857 total time=   0.1s
[CV 5/5; 299/400] START max_depth=8, min_samples_leaf=2, n_estimators=9.........
[CV 5/5; 299/400] END max_depth=8, min_samples_leaf=2, n_estimators=9;, score=0.851 total time=   0.1s
[CV 1/5; 300/400] START max_depth=8, min_samples_leaf=2, n_estimators=10........
[CV 1/5; 300/400] END max_depth=8, min_samples_leaf=2, n_estimators=10;, score=0.849 total time=   0.1s
[CV 2/5; 300/400] START max_depth=8, min_samples_leaf=2, n_estimators=10........
[CV 2/5; 300/400] END max_depth=8, min_samples_leaf=2, n_estimators=10;, score=0.844 total time=   0.1s
[CV 3/5; 300/400] START max_depth=8, min_samples_leaf=2, n_estimators=10........
[CV 3/5; 300/400] END max_depth=8, min_samples_leaf=2, n_estimators=10;, score=0.843 total time=   0.1s
[CV 4/5; 300/400] START max_depth=8, min_samples_leaf=2, n_estimators=10........
[CV 4/5; 300/400] END max_depth=8, min_samples_leaf=2, n_estimators=10;, score=0.842 total time=   0.1s
[CV 5/5; 300/400] START max_depth=8, min_samples_leaf=2, n_estimators=10........
[CV 5/5; 300/400] END max_depth=8, min_samples_leaf=2, n_estimators=10;, score=0.842 total time=   0.1s
[CV 1/5; 301/400] START max_depth=8, min_samples_leaf=3, n_estimators=1.........
[CV 1/5; 301/400] END max_depth=8, min_samples_leaf=3, n_estimators=1;, score=0.794 total time=   0.0s
[CV 2/5; 301/400] START max_depth=8, min_samples_leaf=3, n_estimators=1.........
[CV 2/5; 301/400] END max_depth=8, min_samples_leaf=3, n_estimators=1;, score=0.791 total time=   0.0s
[CV 3/5; 301/400] START max_depth=8, min_samples_leaf=3, n_estimators=1.........
[CV 3/5; 301/400] END max_depth=8, min_samples_leaf=3, n_estimators=1;, score=0.802 total time=   0.0s
[CV 4/5; 301/400] START max_depth=8, min_samples_leaf=3, n_estimators=1.........
[CV 4/5; 301/400] END max_depth=8, min_samples_leaf=3, n_estimators=1;, score=0.803 total time=   0.0s
[CV 5/5; 301/400] START max_depth=8, min_samples_leaf=3, n_estimators=1.........
[CV 5/5; 301/400] END max_depth=8, min_samples_leaf=3, n_estimators=1;, score=0.804 total time=   0.0s
[CV 1/5; 302/400] START max_depth=8, min_samples_leaf=3, n_estimators=2.........
[CV 1/5; 302/400] END max_depth=8, min_samples_leaf=3, n_estimators=2;, score=0.819 total time=   0.0s
[CV 2/5; 302/400] START max_depth=8, min_samples_leaf=3, n_estimators=2.........
[CV 2/5; 302/400] END max_depth=8, min_samples_leaf=3, n_estimators=2;, score=0.819 total time=   0.0s
[CV 3/5; 302/400] START max_depth=8, min_samples_leaf=3, n_estimators=2.........
[CV 3/5; 302/400] END max_depth=8, min_samples_leaf=3, n_estimators=2;, score=0.836 total time=   0.0s
[CV 4/5; 302/400] START max_depth=8, min_samples_leaf=3, n_estimators=2.........
[CV 4/5; 302/400] END max_depth=8, min_samples_leaf=3, n_estimators=2;, score=0.833 total time=   0.0s
[CV 5/5; 302/400] START max_depth=8, min_samples_leaf=3, n_estimators=2.........
[CV 5/5; 302/400] END max_depth=8, min_samples_leaf=3, n_estimators=2;, score=0.839 total time=   0.0s
[CV 1/5; 303/400] START max_depth=8, min_samples_leaf=3, n_estimators=3.........
[CV 1/5; 303/400] END max_depth=8, min_samples_leaf=3, n_estimators=3;, score=0.828 total time=   0.0s
[CV 2/5; 303/400] START max_depth=8, min_samples_leaf=3, n_estimators=3.........
[CV 2/5; 303/400] END max_depth=8, min_samples_leaf=3, n_estimators=3;, score=0.828 total time=   0.0s
[CV 3/5; 303/400] START max_depth=8, min_samples_leaf=3, n_estimators=3.........
[CV 3/5; 303/400] END max_depth=8, min_samples_leaf=3, n_estimators=3;, score=0.836 total time=   0.0s
[CV 4/5; 303/400] START max_depth=8, min_samples_leaf=3, n_estimators=3.........
[CV 4/5; 303/400] END max_depth=8, min_samples_leaf=3, n_estimators=3;, score=0.835 total time=   0.0s
[CV 5/5; 303/400] START max_depth=8, min_samples_leaf=3, n_estimators=3.........
[CV 5/5; 303/400] END max_depth=8, min_samples_leaf=3, n_estimators=3;, score=0.830 total time=   0.0s
[CV 1/5; 304/400] START max_depth=8, min_samples_leaf=3, n_estimators=4.........
[CV 1/5; 304/400] END max_depth=8, min_samples_leaf=3, n_estimators=4;, score=0.843 total time=   0.0s
[CV 2/5; 304/400] START max_depth=8, min_samples_leaf=3, n_estimators=4.........
[CV 2/5; 304/400] END max_depth=8, min_samples_leaf=3, n_estimators=4;, score=0.848 total time=   0.0s
[CV 3/5; 304/400] START max_depth=8, min_samples_leaf=3, n_estimators=4.........
[CV 3/5; 304/400] END max_depth=8, min_samples_leaf=3, n_estimators=4;, score=0.834 total time=   0.0s
[CV 4/5; 304/400] START max_depth=8, min_samples_leaf=3, n_estimators=4.........
[CV 4/5; 304/400] END max_depth=8, min_samples_leaf=3, n_estimators=4;, score=0.846 total time=   0.0s
[CV 5/5; 304/400] START max_depth=8, min_samples_leaf=3, n_estimators=4.........
[CV 5/5; 304/400] END max_depth=8, min_samples_leaf=3, n_estimators=4;, score=0.826 total time=   0.1s
[CV 1/5; 305/400] START max_depth=8, min_samples_leaf=3, n_estimators=5.........
[CV 1/5; 305/400] END max_depth=8, min_samples_leaf=3, n_estimators=5;, score=0.841 total time=   0.1s
[CV 2/5; 305/400] START max_depth=8, min_samples_leaf=3, n_estimators=5.........
[CV 2/5; 305/400] END max_depth=8, min_samples_leaf=3, n_estimators=5;, score=0.847 total time=   0.1s
[CV 3/5; 305/400] START max_depth=8, min_samples_leaf=3, n_estimators=5.........
[CV 3/5; 305/400] END max_depth=8, min_samples_leaf=3, n_estimators=5;, score=0.832 total time=   0.1s
[CV 4/5; 305/400] START max_depth=8, min_samples_leaf=3, n_estimators=5.........
[CV 4/5; 305/400] END max_depth=8, min_samples_leaf=3, n_estimators=5;, score=0.831 total time=   0.1s
[CV 5/5; 305/400] START max_depth=8, min_samples_leaf=3, n_estimators=5.........
[CV 5/5; 305/400] END max_depth=8, min_samples_leaf=3, n_estimators=5;, score=0.818 total time=   0.1s
[CV 1/5; 306/400] START max_depth=8, min_samples_leaf=3, n_estimators=6.........
[CV 1/5; 306/400] END max_depth=8, min_samples_leaf=3, n_estimators=6;, score=0.838 total time=   0.1s
[CV 2/5; 306/400] START max_depth=8, min_samples_leaf=3, n_estimators=6.........
[CV 2/5; 306/400] END max_depth=8, min_samples_leaf=3, n_estimators=6;, score=0.841 total time=   0.1s
[CV 3/5; 306/400] START max_depth=8, min_samples_leaf=3, n_estimators=6.........
[CV 3/5; 306/400] END max_depth=8, min_samples_leaf=3, n_estimators=6;, score=0.829 total time=   0.1s
[CV 4/5; 306/400] START max_depth=8, min_samples_leaf=3, n_estimators=6.........
[CV 4/5; 306/400] END max_depth=8, min_samples_leaf=3, n_estimators=6;, score=0.849 total time=   0.1s
[CV 5/5; 306/400] START max_depth=8, min_samples_leaf=3, n_estimators=6.........
[CV 5/5; 306/400] END max_depth=8, min_samples_leaf=3, n_estimators=6;, score=0.841 total time=   0.1s
[CV 1/5; 307/400] START max_depth=8, min_samples_leaf=3, n_estimators=7.........
[CV 1/5; 307/400] END max_depth=8, min_samples_leaf=3, n_estimators=7;, score=0.848 total time=   0.1s
[CV 2/5; 307/400] START max_depth=8, min_samples_leaf=3, n_estimators=7.........
[CV 2/5; 307/400] END max_depth=8, min_samples_leaf=3, n_estimators=7;, score=0.830 total time=   0.1s
[CV 3/5; 307/400] START max_depth=8, min_samples_leaf=3, n_estimators=7.........
[CV 3/5; 307/400] END max_depth=8, min_samples_leaf=3, n_estimators=7;, score=0.847 total time=   0.1s
[CV 4/5; 307/400] START max_depth=8, min_samples_leaf=3, n_estimators=7.........
[CV 4/5; 307/400] END max_depth=8, min_samples_leaf=3, n_estimators=7;, score=0.837 total time=   0.1s
[CV 5/5; 307/400] START max_depth=8, min_samples_leaf=3, n_estimators=7.........
[CV 5/5; 307/400] END max_depth=8, min_samples_leaf=3, n_estimators=7;, score=0.838 total time=   0.0s
[CV 1/5; 308/400] START max_depth=8, min_samples_leaf=3, n_estimators=8.........
[CV 1/5; 308/400] END max_depth=8, min_samples_leaf=3, n_estimators=8;, score=0.836 total time=   0.1s
[CV 2/5; 308/400] START max_depth=8, min_samples_leaf=3, n_estimators=8.........
[CV 2/5; 308/400] END max_depth=8, min_samples_leaf=3, n_estimators=8;, score=0.846 total time=   0.1s
[CV 3/5; 308/400] START max_depth=8, min_samples_leaf=3, n_estimators=8.........
[CV 3/5; 308/400] END max_depth=8, min_samples_leaf=3, n_estimators=8;, score=0.837 total time=   0.1s
[CV 4/5; 308/400] START max_depth=8, min_samples_leaf=3, n_estimators=8.........
[CV 4/5; 308/400] END max_depth=8, min_samples_leaf=3, n_estimators=8;, score=0.851 total time=   0.1s
[CV 5/5; 308/400] START max_depth=8, min_samples_leaf=3, n_estimators=8.........
[CV 5/5; 308/400] END max_depth=8, min_samples_leaf=3, n_estimators=8;, score=0.840 total time=   0.1s
[CV 1/5; 309/400] START max_depth=8, min_samples_leaf=3, n_estimators=9.........
[CV 1/5; 309/400] END max_depth=8, min_samples_leaf=3, n_estimators=9;, score=0.839 total time=   0.1s
[CV 2/5; 309/400] START max_depth=8, min_samples_leaf=3, n_estimators=9.........
[CV 2/5; 309/400] END max_depth=8, min_samples_leaf=3, n_estimators=9;, score=0.849 total time=   0.1s
[CV 3/5; 309/400] START max_depth=8, min_samples_leaf=3, n_estimators=9.........
[CV 3/5; 309/400] END max_depth=8, min_samples_leaf=3, n_estimators=9;, score=0.843 total time=   0.1s
[CV 4/5; 309/400] START max_depth=8, min_samples_leaf=3, n_estimators=9.........
[CV 4/5; 309/400] END max_depth=8, min_samples_leaf=3, n_estimators=9;, score=0.851 total time=   0.1s
[CV 5/5; 309/400] START max_depth=8, min_samples_leaf=3, n_estimators=9.........
[CV 5/5; 309/400] END max_depth=8, min_samples_leaf=3, n_estimators=9;, score=0.829 total time=   0.1s
[CV 1/5; 310/400] START max_depth=8, min_samples_leaf=3, n_estimators=10........
[CV 1/5; 310/400] END max_depth=8, min_samples_leaf=3, n_estimators=10;, score=0.846 total time=   0.1s
[CV 2/5; 310/400] START max_depth=8, min_samples_leaf=3, n_estimators=10........
[CV 2/5; 310/400] END max_depth=8, min_samples_leaf=3, n_estimators=10;, score=0.837 total time=   0.1s
[CV 3/5; 310/400] START max_depth=8, min_samples_leaf=3, n_estimators=10........
[CV 3/5; 310/400] END max_depth=8, min_samples_leaf=3, n_estimators=10;, score=0.855 total time=   0.1s
[CV 4/5; 310/400] START max_depth=8, min_samples_leaf=3, n_estimators=10........
[CV 4/5; 310/400] END max_depth=8, min_samples_leaf=3, n_estimators=10;, score=0.833 total time=   0.1s
[CV 5/5; 310/400] START max_depth=8, min_samples_leaf=3, n_estimators=10........
[CV 5/5; 310/400] END max_depth=8, min_samples_leaf=3, n_estimators=10;, score=0.842 total time=   0.1s
[CV 1/5; 311/400] START max_depth=8, min_samples_leaf=4, n_estimators=1.........
[CV 1/5; 311/400] END max_depth=8, min_samples_leaf=4, n_estimators=1;, score=0.803 total time=   0.0s
[CV 2/5; 311/400] START max_depth=8, min_samples_leaf=4, n_estimators=1.........
[CV 2/5; 311/400] END max_depth=8, min_samples_leaf=4, n_estimators=1;, score=0.777 total time=   0.0s
[CV 3/5; 311/400] START max_depth=8, min_samples_leaf=4, n_estimators=1.........
[CV 3/5; 311/400] END max_depth=8, min_samples_leaf=4, n_estimators=1;, score=0.786 total time=   0.0s
[CV 4/5; 311/400] START max_depth=8, min_samples_leaf=4, n_estimators=1.........
[CV 4/5; 311/400] END max_depth=8, min_samples_leaf=4, n_estimators=1;, score=0.796 total time=   0.0s
[CV 5/5; 311/400] START max_depth=8, min_samples_leaf=4, n_estimators=1.........
[CV 5/5; 311/400] END max_depth=8, min_samples_leaf=4, n_estimators=1;, score=0.819 total time=   0.0s
[CV 1/5; 312/400] START max_depth=8, min_samples_leaf=4, n_estimators=2.........
[CV 1/5; 312/400] END max_depth=8, min_samples_leaf=4, n_estimators=2;, score=0.834 total time=   0.0s
[CV 2/5; 312/400] START max_depth=8, min_samples_leaf=4, n_estimators=2.........
[CV 2/5; 312/400] END max_depth=8, min_samples_leaf=4, n_estimators=2;, score=0.825 total time=   0.0s
[CV 3/5; 312/400] START max_depth=8, min_samples_leaf=4, n_estimators=2.........
[CV 3/5; 312/400] END max_depth=8, min_samples_leaf=4, n_estimators=2;, score=0.825 total time=   0.0s
[CV 4/5; 312/400] START max_depth=8, min_samples_leaf=4, n_estimators=2.........
[CV 4/5; 312/400] END max_depth=8, min_samples_leaf=4, n_estimators=2;, score=0.834 total time=   0.0s
[CV 5/5; 312/400] START max_depth=8, min_samples_leaf=4, n_estimators=2.........
[CV 5/5; 312/400] END max_depth=8, min_samples_leaf=4, n_estimators=2;, score=0.821 total time=   0.0s
[CV 1/5; 313/400] START max_depth=8, min_samples_leaf=4, n_estimators=3.........
[CV 1/5; 313/400] END max_depth=8, min_samples_leaf=4, n_estimators=3;, score=0.854 total time=   0.0s
[CV 2/5; 313/400] START max_depth=8, min_samples_leaf=4, n_estimators=3.........
[CV 2/5; 313/400] END max_depth=8, min_samples_leaf=4, n_estimators=3;, score=0.840 total time=   0.0s
[CV 3/5; 313/400] START max_depth=8, min_samples_leaf=4, n_estimators=3.........
[CV 3/5; 313/400] END max_depth=8, min_samples_leaf=4, n_estimators=3;, score=0.829 total time=   0.0s
[CV 4/5; 313/400] START max_depth=8, min_samples_leaf=4, n_estimators=3.........
[CV 4/5; 313/400] END max_depth=8, min_samples_leaf=4, n_estimators=3;, score=0.841 total time=   0.0s
[CV 5/5; 313/400] START max_depth=8, min_samples_leaf=4, n_estimators=3.........
[CV 5/5; 313/400] END max_depth=8, min_samples_leaf=4, n_estimators=3;, score=0.827 total time=   0.0s
[CV 1/5; 314/400] START max_depth=8, min_samples_leaf=4, n_estimators=4.........
[CV 1/5; 314/400] END max_depth=8, min_samples_leaf=4, n_estimators=4;, score=0.838 total time=   0.0s
[CV 2/5; 314/400] START max_depth=8, min_samples_leaf=4, n_estimators=4.........
[CV 2/5; 314/400] END max_depth=8, min_samples_leaf=4, n_estimators=4;, score=0.843 total time=   0.0s
[CV 3/5; 314/400] START max_depth=8, min_samples_leaf=4, n_estimators=4.........
[CV 3/5; 314/400] END max_depth=8, min_samples_leaf=4, n_estimators=4;, score=0.837 total time=   0.0s
[CV 4/5; 314/400] START max_depth=8, min_samples_leaf=4, n_estimators=4.........
[CV 4/5; 314/400] END max_depth=8, min_samples_leaf=4, n_estimators=4;, score=0.842 total time=   0.0s
[CV 5/5; 314/400] START max_depth=8, min_samples_leaf=4, n_estimators=4.........
[CV 5/5; 314/400] END max_depth=8, min_samples_leaf=4, n_estimators=4;, score=0.829 total time=   0.0s
[CV 1/5; 315/400] START max_depth=8, min_samples_leaf=4, n_estimators=5.........
[CV 1/5; 315/400] END max_depth=8, min_samples_leaf=4, n_estimators=5;, score=0.847 total time=   0.0s
[CV 2/5; 315/400] START max_depth=8, min_samples_leaf=4, n_estimators=5.........
[CV 2/5; 315/400] END max_depth=8, min_samples_leaf=4, n_estimators=5;, score=0.846 total time=   0.0s
[CV 3/5; 315/400] START max_depth=8, min_samples_leaf=4, n_estimators=5.........
[CV 3/5; 315/400] END max_depth=8, min_samples_leaf=4, n_estimators=5;, score=0.841 total time=   0.0s
[CV 4/5; 315/400] START max_depth=8, min_samples_leaf=4, n_estimators=5.........
[CV 4/5; 315/400] END max_depth=8, min_samples_leaf=4, n_estimators=5;, score=0.845 total time=   0.0s
[CV 5/5; 315/400] START max_depth=8, min_samples_leaf=4, n_estimators=5.........
[CV 5/5; 315/400] END max_depth=8, min_samples_leaf=4, n_estimators=5;, score=0.841 total time=   0.0s
[CV 1/5; 316/400] START max_depth=8, min_samples_leaf=4, n_estimators=6.........
[CV 1/5; 316/400] END max_depth=8, min_samples_leaf=4, n_estimators=6;, score=0.833 total time=   0.0s
[CV 2/5; 316/400] START max_depth=8, min_samples_leaf=4, n_estimators=6.........
[CV 2/5; 316/400] END max_depth=8, min_samples_leaf=4, n_estimators=6;, score=0.841 total time=   0.0s
[CV 3/5; 316/400] START max_depth=8, min_samples_leaf=4, n_estimators=6.........
[CV 3/5; 316/400] END max_depth=8, min_samples_leaf=4, n_estimators=6;, score=0.841 total time=   0.0s
[CV 4/5; 316/400] START max_depth=8, min_samples_leaf=4, n_estimators=6.........
[CV 4/5; 316/400] END max_depth=8, min_samples_leaf=4, n_estimators=6;, score=0.840 total time=   0.0s
[CV 5/5; 316/400] START max_depth=8, min_samples_leaf=4, n_estimators=6.........
[CV 5/5; 316/400] END max_depth=8, min_samples_leaf=4, n_estimators=6;, score=0.840 total time=   0.0s
[CV 1/5; 317/400] START max_depth=8, min_samples_leaf=4, n_estimators=7.........
[CV 1/5; 317/400] END max_depth=8, min_samples_leaf=4, n_estimators=7;, score=0.843 total time=   0.1s
[CV 2/5; 317/400] START max_depth=8, min_samples_leaf=4, n_estimators=7.........
[CV 2/5; 317/400] END max_depth=8, min_samples_leaf=4, n_estimators=7;, score=0.838 total time=   0.0s
[CV 3/5; 317/400] START max_depth=8, min_samples_leaf=4, n_estimators=7.........
[CV 3/5; 317/400] END max_depth=8, min_samples_leaf=4, n_estimators=7;, score=0.843 total time=   0.0s
[CV 4/5; 317/400] START max_depth=8, min_samples_leaf=4, n_estimators=7.........
[CV 4/5; 317/400] END max_depth=8, min_samples_leaf=4, n_estimators=7;, score=0.834 total time=   0.0s
[CV 5/5; 317/400] START max_depth=8, min_samples_leaf=4, n_estimators=7.........
[CV 5/5; 317/400] END max_depth=8, min_samples_leaf=4, n_estimators=7;, score=0.849 total time=   0.1s
[CV 1/5; 318/400] START max_depth=8, min_samples_leaf=4, n_estimators=8.........
[CV 1/5; 318/400] END max_depth=8, min_samples_leaf=4, n_estimators=8;, score=0.844 total time=   0.1s
[CV 2/5; 318/400] START max_depth=8, min_samples_leaf=4, n_estimators=8.........
[CV 2/5; 318/400] END max_depth=8, min_samples_leaf=4, n_estimators=8;, score=0.839 total time=   0.1s
[CV 3/5; 318/400] START max_depth=8, min_samples_leaf=4, n_estimators=8.........
[CV 3/5; 318/400] END max_depth=8, min_samples_leaf=4, n_estimators=8;, score=0.836 total time=   0.1s
[CV 4/5; 318/400] START max_depth=8, min_samples_leaf=4, n_estimators=8.........
[CV 4/5; 318/400] END max_depth=8, min_samples_leaf=4, n_estimators=8;, score=0.846 total time=   0.1s
[CV 5/5; 318/400] START max_depth=8, min_samples_leaf=4, n_estimators=8.........
[CV 5/5; 318/400] END max_depth=8, min_samples_leaf=4, n_estimators=8;, score=0.839 total time=   0.1s
[CV 1/5; 319/400] START max_depth=8, min_samples_leaf=4, n_estimators=9.........
[CV 1/5; 319/400] END max_depth=8, min_samples_leaf=4, n_estimators=9;, score=0.851 total time=   0.1s
[CV 2/5; 319/400] START max_depth=8, min_samples_leaf=4, n_estimators=9.........
[CV 2/5; 319/400] END max_depth=8, min_samples_leaf=4, n_estimators=9;, score=0.854 total time=   0.1s
[CV 3/5; 319/400] START max_depth=8, min_samples_leaf=4, n_estimators=9.........
[CV 3/5; 319/400] END max_depth=8, min_samples_leaf=4, n_estimators=9;, score=0.841 total time=   0.1s
[CV 4/5; 319/400] START max_depth=8, min_samples_leaf=4, n_estimators=9.........
[CV 4/5; 319/400] END max_depth=8, min_samples_leaf=4, n_estimators=9;, score=0.845 total time=   0.1s
[CV 5/5; 319/400] START max_depth=8, min_samples_leaf=4, n_estimators=9.........
[CV 5/5; 319/400] END max_depth=8, min_samples_leaf=4, n_estimators=9;, score=0.842 total time=   0.1s
[CV 1/5; 320/400] START max_depth=8, min_samples_leaf=4, n_estimators=10........
[CV 1/5; 320/400] END max_depth=8, min_samples_leaf=4, n_estimators=10;, score=0.847 total time=   0.1s
[CV 2/5; 320/400] START max_depth=8, min_samples_leaf=4, n_estimators=10........
[CV 2/5; 320/400] END max_depth=8, min_samples_leaf=4, n_estimators=10;, score=0.835 total time=   0.1s
[CV 3/5; 320/400] START max_depth=8, min_samples_leaf=4, n_estimators=10........
[CV 3/5; 320/400] END max_depth=8, min_samples_leaf=4, n_estimators=10;, score=0.844 total time=   0.1s
[CV 4/5; 320/400] START max_depth=8, min_samples_leaf=4, n_estimators=10........
[CV 4/5; 320/400] END max_depth=8, min_samples_leaf=4, n_estimators=10;, score=0.848 total time=   0.1s
[CV 5/5; 320/400] START max_depth=8, min_samples_leaf=4, n_estimators=10........
[CV 5/5; 320/400] END max_depth=8, min_samples_leaf=4, n_estimators=10;, score=0.841 total time=   0.1s
[CV 1/5; 321/400] START max_depth=9, min_samples_leaf=1, n_estimators=1.........
[CV 1/5; 321/400] END max_depth=9, min_samples_leaf=1, n_estimators=1;, score=0.843 total time=   0.0s
[CV 2/5; 321/400] START max_depth=9, min_samples_leaf=1, n_estimators=1.........
[CV 2/5; 321/400] END max_depth=9, min_samples_leaf=1, n_estimators=1;, score=0.822 total time=   0.0s
[CV 3/5; 321/400] START max_depth=9, min_samples_leaf=1, n_estimators=1.........
[CV 3/5; 321/400] END max_depth=9, min_samples_leaf=1, n_estimators=1;, score=0.831 total time=   0.0s
[CV 4/5; 321/400] START max_depth=9, min_samples_leaf=1, n_estimators=1.........
[CV 4/5; 321/400] END max_depth=9, min_samples_leaf=1, n_estimators=1;, score=0.773 total time=   0.0s
[CV 5/5; 321/400] START max_depth=9, min_samples_leaf=1, n_estimators=1.........
[CV 5/5; 321/400] END max_depth=9, min_samples_leaf=1, n_estimators=1;, score=0.798 total time=   0.0s
[CV 1/5; 322/400] START max_depth=9, min_samples_leaf=1, n_estimators=2.........
[CV 1/5; 322/400] END max_depth=9, min_samples_leaf=1, n_estimators=2;, score=0.821 total time=   0.0s
[CV 2/5; 322/400] START max_depth=9, min_samples_leaf=1, n_estimators=2.........
[CV 2/5; 322/400] END max_depth=9, min_samples_leaf=1, n_estimators=2;, score=0.833 total time=   0.0s
[CV 3/5; 322/400] START max_depth=9, min_samples_leaf=1, n_estimators=2.........
[CV 3/5; 322/400] END max_depth=9, min_samples_leaf=1, n_estimators=2;, score=0.834 total time=   0.0s
[CV 4/5; 322/400] START max_depth=9, min_samples_leaf=1, n_estimators=2.........
[CV 4/5; 322/400] END max_depth=9, min_samples_leaf=1, n_estimators=2;, score=0.844 total time=   0.0s
[CV 5/5; 322/400] START max_depth=9, min_samples_leaf=1, n_estimators=2.........
[CV 5/5; 322/400] END max_depth=9, min_samples_leaf=1, n_estimators=2;, score=0.832 total time=   0.0s
[CV 1/5; 323/400] START max_depth=9, min_samples_leaf=1, n_estimators=3.........
[CV 1/5; 323/400] END max_depth=9, min_samples_leaf=1, n_estimators=3;, score=0.851 total time=   0.0s
[CV 2/5; 323/400] START max_depth=9, min_samples_leaf=1, n_estimators=3.........
[CV 2/5; 323/400] END max_depth=9, min_samples_leaf=1, n_estimators=3;, score=0.843 total time=   0.0s
[CV 3/5; 323/400] START max_depth=9, min_samples_leaf=1, n_estimators=3.........
[CV 3/5; 323/400] END max_depth=9, min_samples_leaf=1, n_estimators=3;, score=0.841 total time=   0.0s
[CV 4/5; 323/400] START max_depth=9, min_samples_leaf=1, n_estimators=3.........
[CV 4/5; 323/400] END max_depth=9, min_samples_leaf=1, n_estimators=3;, score=0.847 total time=   0.0s
[CV 5/5; 323/400] START max_depth=9, min_samples_leaf=1, n_estimators=3.........
[CV 5/5; 323/400] END max_depth=9, min_samples_leaf=1, n_estimators=3;, score=0.844 total time=   0.0s
[CV 1/5; 324/400] START max_depth=9, min_samples_leaf=1, n_estimators=4.........
[CV 1/5; 324/400] END max_depth=9, min_samples_leaf=1, n_estimators=4;, score=0.858 total time=   0.0s
[CV 2/5; 324/400] START max_depth=9, min_samples_leaf=1, n_estimators=4.........
[CV 2/5; 324/400] END max_depth=9, min_samples_leaf=1, n_estimators=4;, score=0.857 total time=   0.0s
[CV 3/5; 324/400] START max_depth=9, min_samples_leaf=1, n_estimators=4.........
[CV 3/5; 324/400] END max_depth=9, min_samples_leaf=1, n_estimators=4;, score=0.844 total time=   0.0s
[CV 4/5; 324/400] START max_depth=9, min_samples_leaf=1, n_estimators=4.........
[CV 4/5; 324/400] END max_depth=9, min_samples_leaf=1, n_estimators=4;, score=0.857 total time=   0.0s
[CV 5/5; 324/400] START max_depth=9, min_samples_leaf=1, n_estimators=4.........
[CV 5/5; 324/400] END max_depth=9, min_samples_leaf=1, n_estimators=4;, score=0.852 total time=   0.0s
[CV 1/5; 325/400] START max_depth=9, min_samples_leaf=1, n_estimators=5.........
[CV 1/5; 325/400] END max_depth=9, min_samples_leaf=1, n_estimators=5;, score=0.846 total time=   0.0s
[CV 2/5; 325/400] START max_depth=9, min_samples_leaf=1, n_estimators=5.........
[CV 2/5; 325/400] END max_depth=9, min_samples_leaf=1, n_estimators=5;, score=0.851 total time=   0.0s
[CV 3/5; 325/400] START max_depth=9, min_samples_leaf=1, n_estimators=5.........
[CV 3/5; 325/400] END max_depth=9, min_samples_leaf=1, n_estimators=5;, score=0.861 total time=   0.0s
[CV 4/5; 325/400] START max_depth=9, min_samples_leaf=1, n_estimators=5.........
[CV 4/5; 325/400] END max_depth=9, min_samples_leaf=1, n_estimators=5;, score=0.849 total time=   0.0s
[CV 5/5; 325/400] START max_depth=9, min_samples_leaf=1, n_estimators=5.........
[CV 5/5; 325/400] END max_depth=9, min_samples_leaf=1, n_estimators=5;, score=0.860 total time=   0.0s
[CV 1/5; 326/400] START max_depth=9, min_samples_leaf=1, n_estimators=6.........
[CV 1/5; 326/400] END max_depth=9, min_samples_leaf=1, n_estimators=6;, score=0.855 total time=   0.0s
[CV 2/5; 326/400] START max_depth=9, min_samples_leaf=1, n_estimators=6.........
[CV 2/5; 326/400] END max_depth=9, min_samples_leaf=1, n_estimators=6;, score=0.860 total time=   0.0s
[CV 3/5; 326/400] START max_depth=9, min_samples_leaf=1, n_estimators=6.........
[CV 3/5; 326/400] END max_depth=9, min_samples_leaf=1, n_estimators=6;, score=0.851 total time=   0.0s
[CV 4/5; 326/400] START max_depth=9, min_samples_leaf=1, n_estimators=6.........
[CV 4/5; 326/400] END max_depth=9, min_samples_leaf=1, n_estimators=6;, score=0.859 total time=   0.1s
[CV 5/5; 326/400] START max_depth=9, min_samples_leaf=1, n_estimators=6.........
[CV 5/5; 326/400] END max_depth=9, min_samples_leaf=1, n_estimators=6;, score=0.848 total time=   0.0s
[CV 1/5; 327/400] START max_depth=9, min_samples_leaf=1, n_estimators=7.........
[CV 1/5; 327/400] END max_depth=9, min_samples_leaf=1, n_estimators=7;, score=0.868 total time=   0.1s
[CV 2/5; 327/400] START max_depth=9, min_samples_leaf=1, n_estimators=7.........
[CV 2/5; 327/400] END max_depth=9, min_samples_leaf=1, n_estimators=7;, score=0.849 total time=   0.1s
[CV 3/5; 327/400] START max_depth=9, min_samples_leaf=1, n_estimators=7.........
[CV 3/5; 327/400] END max_depth=9, min_samples_leaf=1, n_estimators=7;, score=0.858 total time=   0.1s
[CV 4/5; 327/400] START max_depth=9, min_samples_leaf=1, n_estimators=7.........
[CV 4/5; 327/400] END max_depth=9, min_samples_leaf=1, n_estimators=7;, score=0.868 total time=   0.1s
[CV 5/5; 327/400] START max_depth=9, min_samples_leaf=1, n_estimators=7.........
[CV 5/5; 327/400] END max_depth=9, min_samples_leaf=1, n_estimators=7;, score=0.862 total time=   0.1s
[CV 1/5; 328/400] START max_depth=9, min_samples_leaf=1, n_estimators=8.........
[CV 1/5; 328/400] END max_depth=9, min_samples_leaf=1, n_estimators=8;, score=0.865 total time=   0.1s
[CV 2/5; 328/400] START max_depth=9, min_samples_leaf=1, n_estimators=8.........
[CV 2/5; 328/400] END max_depth=9, min_samples_leaf=1, n_estimators=8;, score=0.864 total time=   0.1s
[CV 3/5; 328/400] START max_depth=9, min_samples_leaf=1, n_estimators=8.........
[CV 3/5; 328/400] END max_depth=9, min_samples_leaf=1, n_estimators=8;, score=0.860 total time=   0.1s
[CV 4/5; 328/400] START max_depth=9, min_samples_leaf=1, n_estimators=8.........
[CV 4/5; 328/400] END max_depth=9, min_samples_leaf=1, n_estimators=8;, score=0.855 total time=   0.1s
[CV 5/5; 328/400] START max_depth=9, min_samples_leaf=1, n_estimators=8.........
[CV 5/5; 328/400] END max_depth=9, min_samples_leaf=1, n_estimators=8;, score=0.852 total time=   0.1s
[CV 1/5; 329/400] START max_depth=9, min_samples_leaf=1, n_estimators=9.........
[CV 1/5; 329/400] END max_depth=9, min_samples_leaf=1, n_estimators=9;, score=0.855 total time=   0.1s
[CV 2/5; 329/400] START max_depth=9, min_samples_leaf=1, n_estimators=9.........
[CV 2/5; 329/400] END max_depth=9, min_samples_leaf=1, n_estimators=9;, score=0.862 total time=   0.1s
[CV 3/5; 329/400] START max_depth=9, min_samples_leaf=1, n_estimators=9.........
[CV 3/5; 329/400] END max_depth=9, min_samples_leaf=1, n_estimators=9;, score=0.854 total time=   0.1s
[CV 4/5; 329/400] START max_depth=9, min_samples_leaf=1, n_estimators=9.........
[CV 4/5; 329/400] END max_depth=9, min_samples_leaf=1, n_estimators=9;, score=0.863 total time=   0.1s
[CV 5/5; 329/400] START max_depth=9, min_samples_leaf=1, n_estimators=9.........
[CV 5/5; 329/400] END max_depth=9, min_samples_leaf=1, n_estimators=9;, score=0.852 total time=   0.1s
[CV 1/5; 330/400] START max_depth=9, min_samples_leaf=1, n_estimators=10........
[CV 1/5; 330/400] END max_depth=9, min_samples_leaf=1, n_estimators=10;, score=0.867 total time=   0.1s
[CV 2/5; 330/400] START max_depth=9, min_samples_leaf=1, n_estimators=10........
[CV 2/5; 330/400] END max_depth=9, min_samples_leaf=1, n_estimators=10;, score=0.863 total time=   0.1s
[CV 3/5; 330/400] START max_depth=9, min_samples_leaf=1, n_estimators=10........
[CV 3/5; 330/400] END max_depth=9, min_samples_leaf=1, n_estimators=10;, score=0.850 total time=   0.1s
[CV 4/5; 330/400] START max_depth=9, min_samples_leaf=1, n_estimators=10........
[CV 4/5; 330/400] END max_depth=9, min_samples_leaf=1, n_estimators=10;, score=0.866 total time=   0.1s
[CV 5/5; 330/400] START max_depth=9, min_samples_leaf=1, n_estimators=10........
[CV 5/5; 330/400] END max_depth=9, min_samples_leaf=1, n_estimators=10;, score=0.851 total time=   0.1s
[CV 1/5; 331/400] START max_depth=9, min_samples_leaf=2, n_estimators=1.........
[CV 1/5; 331/400] END max_depth=9, min_samples_leaf=2, n_estimators=1;, score=0.795 total time=   0.0s
[CV 2/5; 331/400] START max_depth=9, min_samples_leaf=2, n_estimators=1.........
[CV 2/5; 331/400] END max_depth=9, min_samples_leaf=2, n_estimators=1;, score=0.783 total time=   0.0s
[CV 3/5; 331/400] START max_depth=9, min_samples_leaf=2, n_estimators=1.........
[CV 3/5; 331/400] END max_depth=9, min_samples_leaf=2, n_estimators=1;, score=0.807 total time=   0.0s
[CV 4/5; 331/400] START max_depth=9, min_samples_leaf=2, n_estimators=1.........
[CV 4/5; 331/400] END max_depth=9, min_samples_leaf=2, n_estimators=1;, score=0.831 total time=   0.0s
[CV 5/5; 331/400] START max_depth=9, min_samples_leaf=2, n_estimators=1.........
[CV 5/5; 331/400] END max_depth=9, min_samples_leaf=2, n_estimators=1;, score=0.782 total time=   0.0s
[CV 1/5; 332/400] START max_depth=9, min_samples_leaf=2, n_estimators=2.........
[CV 1/5; 332/400] END max_depth=9, min_samples_leaf=2, n_estimators=2;, score=0.827 total time=   0.0s
[CV 2/5; 332/400] START max_depth=9, min_samples_leaf=2, n_estimators=2.........
[CV 2/5; 332/400] END max_depth=9, min_samples_leaf=2, n_estimators=2;, score=0.835 total time=   0.0s
[CV 3/5; 332/400] START max_depth=9, min_samples_leaf=2, n_estimators=2.........
[CV 3/5; 332/400] END max_depth=9, min_samples_leaf=2, n_estimators=2;, score=0.822 total time=   0.0s
[CV 4/5; 332/400] START max_depth=9, min_samples_leaf=2, n_estimators=2.........
[CV 4/5; 332/400] END max_depth=9, min_samples_leaf=2, n_estimators=2;, score=0.846 total time=   0.0s
[CV 5/5; 332/400] START max_depth=9, min_samples_leaf=2, n_estimators=2.........
[CV 5/5; 332/400] END max_depth=9, min_samples_leaf=2, n_estimators=2;, score=0.827 total time=   0.0s
[CV 1/5; 333/400] START max_depth=9, min_samples_leaf=2, n_estimators=3.........
[CV 1/5; 333/400] END max_depth=9, min_samples_leaf=2, n_estimators=3;, score=0.843 total time=   0.0s
[CV 2/5; 333/400] START max_depth=9, min_samples_leaf=2, n_estimators=3.........
[CV 2/5; 333/400] END max_depth=9, min_samples_leaf=2, n_estimators=3;, score=0.842 total time=   0.0s
[CV 3/5; 333/400] START max_depth=9, min_samples_leaf=2, n_estimators=3.........
[CV 3/5; 333/400] END max_depth=9, min_samples_leaf=2, n_estimators=3;, score=0.849 total time=   0.0s
[CV 4/5; 333/400] START max_depth=9, min_samples_leaf=2, n_estimators=3.........
[CV 4/5; 333/400] END max_depth=9, min_samples_leaf=2, n_estimators=3;, score=0.841 total time=   0.0s
[CV 5/5; 333/400] START max_depth=9, min_samples_leaf=2, n_estimators=3.........
[CV 5/5; 333/400] END max_depth=9, min_samples_leaf=2, n_estimators=3;, score=0.835 total time=   0.0s
[CV 1/5; 334/400] START max_depth=9, min_samples_leaf=2, n_estimators=4.........
[CV 1/5; 334/400] END max_depth=9, min_samples_leaf=2, n_estimators=4;, score=0.843 total time=   0.0s
[CV 2/5; 334/400] START max_depth=9, min_samples_leaf=2, n_estimators=4.........
[CV 2/5; 334/400] END max_depth=9, min_samples_leaf=2, n_estimators=4;, score=0.839 total time=   0.0s
[CV 3/5; 334/400] START max_depth=9, min_samples_leaf=2, n_estimators=4.........
[CV 3/5; 334/400] END max_depth=9, min_samples_leaf=2, n_estimators=4;, score=0.853 total time=   0.0s
[CV 4/5; 334/400] START max_depth=9, min_samples_leaf=2, n_estimators=4.........
[CV 4/5; 334/400] END max_depth=9, min_samples_leaf=2, n_estimators=4;, score=0.845 total time=   0.0s
[CV 5/5; 334/400] START max_depth=9, min_samples_leaf=2, n_estimators=4.........
[CV 5/5; 334/400] END max_depth=9, min_samples_leaf=2, n_estimators=4;, score=0.852 total time=   0.0s
[CV 1/5; 335/400] START max_depth=9, min_samples_leaf=2, n_estimators=5.........
[CV 1/5; 335/400] END max_depth=9, min_samples_leaf=2, n_estimators=5;, score=0.858 total time=   0.1s
[CV 2/5; 335/400] START max_depth=9, min_samples_leaf=2, n_estimators=5.........
[CV 2/5; 335/400] END max_depth=9, min_samples_leaf=2, n_estimators=5;, score=0.843 total time=   0.0s
[CV 3/5; 335/400] START max_depth=9, min_samples_leaf=2, n_estimators=5.........
[CV 3/5; 335/400] END max_depth=9, min_samples_leaf=2, n_estimators=5;, score=0.847 total time=   0.0s
[CV 4/5; 335/400] START max_depth=9, min_samples_leaf=2, n_estimators=5.........
[CV 4/5; 335/400] END max_depth=9, min_samples_leaf=2, n_estimators=5;, score=0.850 total time=   0.0s
[CV 5/5; 335/400] START max_depth=9, min_samples_leaf=2, n_estimators=5.........
[CV 5/5; 335/400] END max_depth=9, min_samples_leaf=2, n_estimators=5;, score=0.846 total time=   0.0s
[CV 1/5; 336/400] START max_depth=9, min_samples_leaf=2, n_estimators=6.........
[CV 1/5; 336/400] END max_depth=9, min_samples_leaf=2, n_estimators=6;, score=0.861 total time=   0.1s
[CV 2/5; 336/400] START max_depth=9, min_samples_leaf=2, n_estimators=6.........
[CV 2/5; 336/400] END max_depth=9, min_samples_leaf=2, n_estimators=6;, score=0.843 total time=   0.1s
[CV 3/5; 336/400] START max_depth=9, min_samples_leaf=2, n_estimators=6.........
[CV 3/5; 336/400] END max_depth=9, min_samples_leaf=2, n_estimators=6;, score=0.850 total time=   0.1s
[CV 4/5; 336/400] START max_depth=9, min_samples_leaf=2, n_estimators=6.........
[CV 4/5; 336/400] END max_depth=9, min_samples_leaf=2, n_estimators=6;, score=0.852 total time=   0.1s
[CV 5/5; 336/400] START max_depth=9, min_samples_leaf=2, n_estimators=6.........
[CV 5/5; 336/400] END max_depth=9, min_samples_leaf=2, n_estimators=6;, score=0.865 total time=   0.1s
[CV 1/5; 337/400] START max_depth=9, min_samples_leaf=2, n_estimators=7.........
[CV 1/5; 337/400] END max_depth=9, min_samples_leaf=2, n_estimators=7;, score=0.863 total time=   0.1s
[CV 2/5; 337/400] START max_depth=9, min_samples_leaf=2, n_estimators=7.........
[CV 2/5; 337/400] END max_depth=9, min_samples_leaf=2, n_estimators=7;, score=0.855 total time=   0.1s
[CV 3/5; 337/400] START max_depth=9, min_samples_leaf=2, n_estimators=7.........
[CV 3/5; 337/400] END max_depth=9, min_samples_leaf=2, n_estimators=7;, score=0.863 total time=   0.1s
[CV 4/5; 337/400] START max_depth=9, min_samples_leaf=2, n_estimators=7.........
[CV 4/5; 337/400] END max_depth=9, min_samples_leaf=2, n_estimators=7;, score=0.843 total time=   0.1s
[CV 5/5; 337/400] START max_depth=9, min_samples_leaf=2, n_estimators=7.........
[CV 5/5; 337/400] END max_depth=9, min_samples_leaf=2, n_estimators=7;, score=0.862 total time=   0.1s
[CV 1/5; 338/400] START max_depth=9, min_samples_leaf=2, n_estimators=8.........
[CV 1/5; 338/400] END max_depth=9, min_samples_leaf=2, n_estimators=8;, score=0.856 total time=   0.1s
[CV 2/5; 338/400] START max_depth=9, min_samples_leaf=2, n_estimators=8.........
[CV 2/5; 338/400] END max_depth=9, min_samples_leaf=2, n_estimators=8;, score=0.857 total time=   0.1s
[CV 3/5; 338/400] START max_depth=9, min_samples_leaf=2, n_estimators=8.........
[CV 3/5; 338/400] END max_depth=9, min_samples_leaf=2, n_estimators=8;, score=0.860 total time=   0.1s
[CV 4/5; 338/400] START max_depth=9, min_samples_leaf=2, n_estimators=8.........
[CV 4/5; 338/400] END max_depth=9, min_samples_leaf=2, n_estimators=8;, score=0.863 total time=   0.1s
[CV 5/5; 338/400] START max_depth=9, min_samples_leaf=2, n_estimators=8.........
[CV 5/5; 338/400] END max_depth=9, min_samples_leaf=2, n_estimators=8;, score=0.860 total time=   0.1s
[CV 1/5; 339/400] START max_depth=9, min_samples_leaf=2, n_estimators=9.........
[CV 1/5; 339/400] END max_depth=9, min_samples_leaf=2, n_estimators=9;, score=0.853 total time=   0.1s
[CV 2/5; 339/400] START max_depth=9, min_samples_leaf=2, n_estimators=9.........
[CV 2/5; 339/400] END max_depth=9, min_samples_leaf=2, n_estimators=9;, score=0.862 total time=   0.1s
[CV 3/5; 339/400] START max_depth=9, min_samples_leaf=2, n_estimators=9.........
[CV 3/5; 339/400] END max_depth=9, min_samples_leaf=2, n_estimators=9;, score=0.852 total time=   0.1s
[CV 4/5; 339/400] START max_depth=9, min_samples_leaf=2, n_estimators=9.........
[CV 4/5; 339/400] END max_depth=9, min_samples_leaf=2, n_estimators=9;, score=0.867 total time=   0.1s
[CV 5/5; 339/400] START max_depth=9, min_samples_leaf=2, n_estimators=9.........
[CV 5/5; 339/400] END max_depth=9, min_samples_leaf=2, n_estimators=9;, score=0.861 total time=   0.1s
[CV 1/5; 340/400] START max_depth=9, min_samples_leaf=2, n_estimators=10........
[CV 1/5; 340/400] END max_depth=9, min_samples_leaf=2, n_estimators=10;, score=0.858 total time=   0.1s
[CV 2/5; 340/400] START max_depth=9, min_samples_leaf=2, n_estimators=10........
[CV 2/5; 340/400] END max_depth=9, min_samples_leaf=2, n_estimators=10;, score=0.857 total time=   0.1s
[CV 3/5; 340/400] START max_depth=9, min_samples_leaf=2, n_estimators=10........
[CV 3/5; 340/400] END max_depth=9, min_samples_leaf=2, n_estimators=10;, score=0.862 total time=   0.1s
[CV 4/5; 340/400] START max_depth=9, min_samples_leaf=2, n_estimators=10........
[CV 4/5; 340/400] END max_depth=9, min_samples_leaf=2, n_estimators=10;, score=0.861 total time=   0.1s
[CV 5/5; 340/400] START max_depth=9, min_samples_leaf=2, n_estimators=10........
[CV 5/5; 340/400] END max_depth=9, min_samples_leaf=2, n_estimators=10;, score=0.867 total time=   0.1s
[CV 1/5; 341/400] START max_depth=9, min_samples_leaf=3, n_estimators=1.........
[CV 1/5; 341/400] END max_depth=9, min_samples_leaf=3, n_estimators=1;, score=0.789 total time=   0.0s
[CV 2/5; 341/400] START max_depth=9, min_samples_leaf=3, n_estimators=1.........
[CV 2/5; 341/400] END max_depth=9, min_samples_leaf=3, n_estimators=1;, score=0.810 total time=   0.0s
[CV 3/5; 341/400] START max_depth=9, min_samples_leaf=3, n_estimators=1.........
[CV 3/5; 341/400] END max_depth=9, min_samples_leaf=3, n_estimators=1;, score=0.810 total time=   0.0s
[CV 4/5; 341/400] START max_depth=9, min_samples_leaf=3, n_estimators=1.........
[CV 4/5; 341/400] END max_depth=9, min_samples_leaf=3, n_estimators=1;, score=0.818 total time=   0.0s
[CV 5/5; 341/400] START max_depth=9, min_samples_leaf=3, n_estimators=1.........
[CV 5/5; 341/400] END max_depth=9, min_samples_leaf=3, n_estimators=1;, score=0.786 total time=   0.0s
[CV 1/5; 342/400] START max_depth=9, min_samples_leaf=3, n_estimators=2.........
[CV 1/5; 342/400] END max_depth=9, min_samples_leaf=3, n_estimators=2;, score=0.838 total time=   0.0s
[CV 2/5; 342/400] START max_depth=9, min_samples_leaf=3, n_estimators=2.........
[CV 2/5; 342/400] END max_depth=9, min_samples_leaf=3, n_estimators=2;, score=0.835 total time=   0.0s
[CV 3/5; 342/400] START max_depth=9, min_samples_leaf=3, n_estimators=2.........
[CV 3/5; 342/400] END max_depth=9, min_samples_leaf=3, n_estimators=2;, score=0.840 total time=   0.0s
[CV 4/5; 342/400] START max_depth=9, min_samples_leaf=3, n_estimators=2.........
[CV 4/5; 342/400] END max_depth=9, min_samples_leaf=3, n_estimators=2;, score=0.842 total time=   0.0s
[CV 5/5; 342/400] START max_depth=9, min_samples_leaf=3, n_estimators=2.........
[CV 5/5; 342/400] END max_depth=9, min_samples_leaf=3, n_estimators=2;, score=0.843 total time=   0.0s
[CV 1/5; 343/400] START max_depth=9, min_samples_leaf=3, n_estimators=3.........
[CV 1/5; 343/400] END max_depth=9, min_samples_leaf=3, n_estimators=3;, score=0.841 total time=   0.0s
[CV 2/5; 343/400] START max_depth=9, min_samples_leaf=3, n_estimators=3.........
[CV 2/5; 343/400] END max_depth=9, min_samples_leaf=3, n_estimators=3;, score=0.838 total time=   0.0s
[CV 3/5; 343/400] START max_depth=9, min_samples_leaf=3, n_estimators=3.........
[CV 3/5; 343/400] END max_depth=9, min_samples_leaf=3, n_estimators=3;, score=0.838 total time=   0.0s
[CV 4/5; 343/400] START max_depth=9, min_samples_leaf=3, n_estimators=3.........
[CV 4/5; 343/400] END max_depth=9, min_samples_leaf=3, n_estimators=3;, score=0.841 total time=   0.0s
[CV 5/5; 343/400] START max_depth=9, min_samples_leaf=3, n_estimators=3.........
[CV 5/5; 343/400] END max_depth=9, min_samples_leaf=3, n_estimators=3;, score=0.835 total time=   0.0s
[CV 1/5; 344/400] START max_depth=9, min_samples_leaf=3, n_estimators=4.........
[CV 1/5; 344/400] END max_depth=9, min_samples_leaf=3, n_estimators=4;, score=0.856 total time=   0.0s
[CV 2/5; 344/400] START max_depth=9, min_samples_leaf=3, n_estimators=4.........
[CV 2/5; 344/400] END max_depth=9, min_samples_leaf=3, n_estimators=4;, score=0.832 total time=   0.0s
[CV 3/5; 344/400] START max_depth=9, min_samples_leaf=3, n_estimators=4.........
[CV 3/5; 344/400] END max_depth=9, min_samples_leaf=3, n_estimators=4;, score=0.846 total time=   0.0s
[CV 4/5; 344/400] START max_depth=9, min_samples_leaf=3, n_estimators=4.........
[CV 4/5; 344/400] END max_depth=9, min_samples_leaf=3, n_estimators=4;, score=0.857 total time=   0.0s
[CV 5/5; 344/400] START max_depth=9, min_samples_leaf=3, n_estimators=4.........
[CV 5/5; 344/400] END max_depth=9, min_samples_leaf=3, n_estimators=4;, score=0.838 total time=   0.0s
[CV 1/5; 345/400] START max_depth=9, min_samples_leaf=3, n_estimators=5.........
[CV 1/5; 345/400] END max_depth=9, min_samples_leaf=3, n_estimators=5;, score=0.849 total time=   0.0s
[CV 2/5; 345/400] START max_depth=9, min_samples_leaf=3, n_estimators=5.........
[CV 2/5; 345/400] END max_depth=9, min_samples_leaf=3, n_estimators=5;, score=0.860 total time=   0.0s
[CV 3/5; 345/400] START max_depth=9, min_samples_leaf=3, n_estimators=5.........
[CV 3/5; 345/400] END max_depth=9, min_samples_leaf=3, n_estimators=5;, score=0.846 total time=   0.0s
[CV 4/5; 345/400] START max_depth=9, min_samples_leaf=3, n_estimators=5.........
[CV 4/5; 345/400] END max_depth=9, min_samples_leaf=3, n_estimators=5;, score=0.855 total time=   0.0s
[CV 5/5; 345/400] START max_depth=9, min_samples_leaf=3, n_estimators=5.........
[CV 5/5; 345/400] END max_depth=9, min_samples_leaf=3, n_estimators=5;, score=0.862 total time=   0.0s
[CV 1/5; 346/400] START max_depth=9, min_samples_leaf=3, n_estimators=6.........
[CV 1/5; 346/400] END max_depth=9, min_samples_leaf=3, n_estimators=6;, score=0.852 total time=   0.1s
[CV 2/5; 346/400] START max_depth=9, min_samples_leaf=3, n_estimators=6.........
[CV 2/5; 346/400] END max_depth=9, min_samples_leaf=3, n_estimators=6;, score=0.849 total time=   0.0s
[CV 3/5; 346/400] START max_depth=9, min_samples_leaf=3, n_estimators=6.........
[CV 3/5; 346/400] END max_depth=9, min_samples_leaf=3, n_estimators=6;, score=0.842 total time=   0.0s
[CV 4/5; 346/400] START max_depth=9, min_samples_leaf=3, n_estimators=6.........
[CV 4/5; 346/400] END max_depth=9, min_samples_leaf=3, n_estimators=6;, score=0.852 total time=   0.0s
[CV 5/5; 346/400] START max_depth=9, min_samples_leaf=3, n_estimators=6.........
[CV 5/5; 346/400] END max_depth=9, min_samples_leaf=3, n_estimators=6;, score=0.863 total time=   0.0s
[CV 1/5; 347/400] START max_depth=9, min_samples_leaf=3, n_estimators=7.........
[CV 1/5; 347/400] END max_depth=9, min_samples_leaf=3, n_estimators=7;, score=0.863 total time=   0.1s
[CV 2/5; 347/400] START max_depth=9, min_samples_leaf=3, n_estimators=7.........
[CV 2/5; 347/400] END max_depth=9, min_samples_leaf=3, n_estimators=7;, score=0.845 total time=   0.0s
[CV 3/5; 347/400] START max_depth=9, min_samples_leaf=3, n_estimators=7.........
[CV 3/5; 347/400] END max_depth=9, min_samples_leaf=3, n_estimators=7;, score=0.851 total time=   0.1s
[CV 4/5; 347/400] START max_depth=9, min_samples_leaf=3, n_estimators=7.........
[CV 4/5; 347/400] END max_depth=9, min_samples_leaf=3, n_estimators=7;, score=0.857 total time=   0.1s
[CV 5/5; 347/400] START max_depth=9, min_samples_leaf=3, n_estimators=7.........
[CV 5/5; 347/400] END max_depth=9, min_samples_leaf=3, n_estimators=7;, score=0.862 total time=   0.1s
[CV 1/5; 348/400] START max_depth=9, min_samples_leaf=3, n_estimators=8.........
[CV 1/5; 348/400] END max_depth=9, min_samples_leaf=3, n_estimators=8;, score=0.856 total time=   0.1s
[CV 2/5; 348/400] START max_depth=9, min_samples_leaf=3, n_estimators=8.........
[CV 2/5; 348/400] END max_depth=9, min_samples_leaf=3, n_estimators=8;, score=0.854 total time=   0.1s
[CV 3/5; 348/400] START max_depth=9, min_samples_leaf=3, n_estimators=8.........
[CV 3/5; 348/400] END max_depth=9, min_samples_leaf=3, n_estimators=8;, score=0.859 total time=   0.1s
[CV 4/5; 348/400] START max_depth=9, min_samples_leaf=3, n_estimators=8.........
[CV 4/5; 348/400] END max_depth=9, min_samples_leaf=3, n_estimators=8;, score=0.870 total time=   0.1s
[CV 5/5; 348/400] START max_depth=9, min_samples_leaf=3, n_estimators=8.........
[CV 5/5; 348/400] END max_depth=9, min_samples_leaf=3, n_estimators=8;, score=0.853 total time=   0.1s
[CV 1/5; 349/400] START max_depth=9, min_samples_leaf=3, n_estimators=9.........
[CV 1/5; 349/400] END max_depth=9, min_samples_leaf=3, n_estimators=9;, score=0.860 total time=   0.1s
[CV 2/5; 349/400] START max_depth=9, min_samples_leaf=3, n_estimators=9.........
[CV 2/5; 349/400] END max_depth=9, min_samples_leaf=3, n_estimators=9;, score=0.855 total time=   0.1s
[CV 3/5; 349/400] START max_depth=9, min_samples_leaf=3, n_estimators=9.........
[CV 3/5; 349/400] END max_depth=9, min_samples_leaf=3, n_estimators=9;, score=0.855 total time=   0.1s
[CV 4/5; 349/400] START max_depth=9, min_samples_leaf=3, n_estimators=9.........
[CV 4/5; 349/400] END max_depth=9, min_samples_leaf=3, n_estimators=9;, score=0.857 total time=   0.1s
[CV 5/5; 349/400] START max_depth=9, min_samples_leaf=3, n_estimators=9.........
[CV 5/5; 349/400] END max_depth=9, min_samples_leaf=3, n_estimators=9;, score=0.852 total time=   0.1s
[CV 1/5; 350/400] START max_depth=9, min_samples_leaf=3, n_estimators=10........
[CV 1/5; 350/400] END max_depth=9, min_samples_leaf=3, n_estimators=10;, score=0.856 total time=   0.1s
[CV 2/5; 350/400] START max_depth=9, min_samples_leaf=3, n_estimators=10........
[CV 2/5; 350/400] END max_depth=9, min_samples_leaf=3, n_estimators=10;, score=0.857 total time=   0.1s
[CV 3/5; 350/400] START max_depth=9, min_samples_leaf=3, n_estimators=10........
[CV 3/5; 350/400] END max_depth=9, min_samples_leaf=3, n_estimators=10;, score=0.852 total time=   0.1s
[CV 4/5; 350/400] START max_depth=9, min_samples_leaf=3, n_estimators=10........
[CV 4/5; 350/400] END max_depth=9, min_samples_leaf=3, n_estimators=10;, score=0.855 total time=   0.1s
[CV 5/5; 350/400] START max_depth=9, min_samples_leaf=3, n_estimators=10........
[CV 5/5; 350/400] END max_depth=9, min_samples_leaf=3, n_estimators=10;, score=0.850 total time=   0.1s
[CV 1/5; 351/400] START max_depth=9, min_samples_leaf=4, n_estimators=1.........
[CV 1/5; 351/400] END max_depth=9, min_samples_leaf=4, n_estimators=1;, score=0.816 total time=   0.0s
[CV 2/5; 351/400] START max_depth=9, min_samples_leaf=4, n_estimators=1.........
[CV 2/5; 351/400] END max_depth=9, min_samples_leaf=4, n_estimators=1;, score=0.828 total time=   0.0s
[CV 3/5; 351/400] START max_depth=9, min_samples_leaf=4, n_estimators=1.........
[CV 3/5; 351/400] END max_depth=9, min_samples_leaf=4, n_estimators=1;, score=0.805 total time=   0.0s
[CV 4/5; 351/400] START max_depth=9, min_samples_leaf=4, n_estimators=1.........
[CV 4/5; 351/400] END max_depth=9, min_samples_leaf=4, n_estimators=1;, score=0.812 total time=   0.0s
[CV 5/5; 351/400] START max_depth=9, min_samples_leaf=4, n_estimators=1.........
[CV 5/5; 351/400] END max_depth=9, min_samples_leaf=4, n_estimators=1;, score=0.829 total time=   0.0s
[CV 1/5; 352/400] START max_depth=9, min_samples_leaf=4, n_estimators=2.........
[CV 1/5; 352/400] END max_depth=9, min_samples_leaf=4, n_estimators=2;, score=0.828 total time=   0.0s
[CV 2/5; 352/400] START max_depth=9, min_samples_leaf=4, n_estimators=2.........
[CV 2/5; 352/400] END max_depth=9, min_samples_leaf=4, n_estimators=2;, score=0.830 total time=   0.0s
[CV 3/5; 352/400] START max_depth=9, min_samples_leaf=4, n_estimators=2.........
[CV 3/5; 352/400] END max_depth=9, min_samples_leaf=4, n_estimators=2;, score=0.840 total time=   0.0s
[CV 4/5; 352/400] START max_depth=9, min_samples_leaf=4, n_estimators=2.........
[CV 4/5; 352/400] END max_depth=9, min_samples_leaf=4, n_estimators=2;, score=0.832 total time=   0.0s
[CV 5/5; 352/400] START max_depth=9, min_samples_leaf=4, n_estimators=2.........
[CV 5/5; 352/400] END max_depth=9, min_samples_leaf=4, n_estimators=2;, score=0.837 total time=   0.0s
[CV 1/5; 353/400] START max_depth=9, min_samples_leaf=4, n_estimators=3.........
[CV 1/5; 353/400] END max_depth=9, min_samples_leaf=4, n_estimators=3;, score=0.833 total time=   0.1s
[CV 2/5; 353/400] START max_depth=9, min_samples_leaf=4, n_estimators=3.........
[CV 2/5; 353/400] END max_depth=9, min_samples_leaf=4, n_estimators=3;, score=0.835 total time=   0.0s
[CV 3/5; 353/400] START max_depth=9, min_samples_leaf=4, n_estimators=3.........
[CV 3/5; 353/400] END max_depth=9, min_samples_leaf=4, n_estimators=3;, score=0.843 total time=   0.0s
[CV 4/5; 353/400] START max_depth=9, min_samples_leaf=4, n_estimators=3.........
[CV 4/5; 353/400] END max_depth=9, min_samples_leaf=4, n_estimators=3;, score=0.850 total time=   0.0s
[CV 5/5; 353/400] START max_depth=9, min_samples_leaf=4, n_estimators=3.........
[CV 5/5; 353/400] END max_depth=9, min_samples_leaf=4, n_estimators=3;, score=0.831 total time=   0.0s
[CV 1/5; 354/400] START max_depth=9, min_samples_leaf=4, n_estimators=4.........
[CV 1/5; 354/400] END max_depth=9, min_samples_leaf=4, n_estimators=4;, score=0.860 total time=   0.1s
[CV 2/5; 354/400] START max_depth=9, min_samples_leaf=4, n_estimators=4.........
[CV 2/5; 354/400] END max_depth=9, min_samples_leaf=4, n_estimators=4;, score=0.849 total time=   0.0s
[CV 3/5; 354/400] START max_depth=9, min_samples_leaf=4, n_estimators=4.........
[CV 3/5; 354/400] END max_depth=9, min_samples_leaf=4, n_estimators=4;, score=0.838 total time=   0.0s
[CV 4/5; 354/400] START max_depth=9, min_samples_leaf=4, n_estimators=4.........
[CV 4/5; 354/400] END max_depth=9, min_samples_leaf=4, n_estimators=4;, score=0.842 total time=   0.0s
[CV 5/5; 354/400] START max_depth=9, min_samples_leaf=4, n_estimators=4.........
[CV 5/5; 354/400] END max_depth=9, min_samples_leaf=4, n_estimators=4;, score=0.843 total time=   0.0s
[CV 1/5; 355/400] START max_depth=9, min_samples_leaf=4, n_estimators=5.........
[CV 1/5; 355/400] END max_depth=9, min_samples_leaf=4, n_estimators=5;, score=0.845 total time=   0.1s
[CV 2/5; 355/400] START max_depth=9, min_samples_leaf=4, n_estimators=5.........
[CV 2/5; 355/400] END max_depth=9, min_samples_leaf=4, n_estimators=5;, score=0.851 total time=   0.1s
[CV 3/5; 355/400] START max_depth=9, min_samples_leaf=4, n_estimators=5.........
[CV 3/5; 355/400] END max_depth=9, min_samples_leaf=4, n_estimators=5;, score=0.845 total time=   0.1s
[CV 4/5; 355/400] START max_depth=9, min_samples_leaf=4, n_estimators=5.........
[CV 4/5; 355/400] END max_depth=9, min_samples_leaf=4, n_estimators=5;, score=0.841 total time=   0.1s
[CV 5/5; 355/400] START max_depth=9, min_samples_leaf=4, n_estimators=5.........
[CV 5/5; 355/400] END max_depth=9, min_samples_leaf=4, n_estimators=5;, score=0.849 total time=   0.1s
[CV 1/5; 356/400] START max_depth=9, min_samples_leaf=4, n_estimators=6.........
[CV 1/5; 356/400] END max_depth=9, min_samples_leaf=4, n_estimators=6;, score=0.846 total time=   0.1s
[CV 2/5; 356/400] START max_depth=9, min_samples_leaf=4, n_estimators=6.........
[CV 2/5; 356/400] END max_depth=9, min_samples_leaf=4, n_estimators=6;, score=0.846 total time=   0.1s
[CV 3/5; 356/400] START max_depth=9, min_samples_leaf=4, n_estimators=6.........
[CV 3/5; 356/400] END max_depth=9, min_samples_leaf=4, n_estimators=6;, score=0.846 total time=   0.1s
[CV 4/5; 356/400] START max_depth=9, min_samples_leaf=4, n_estimators=6.........
[CV 4/5; 356/400] END max_depth=9, min_samples_leaf=4, n_estimators=6;, score=0.849 total time=   0.1s
[CV 5/5; 356/400] START max_depth=9, min_samples_leaf=4, n_estimators=6.........
[CV 5/5; 356/400] END max_depth=9, min_samples_leaf=4, n_estimators=6;, score=0.858 total time=   0.1s
[CV 1/5; 357/400] START max_depth=9, min_samples_leaf=4, n_estimators=7.........
[CV 1/5; 357/400] END max_depth=9, min_samples_leaf=4, n_estimators=7;, score=0.851 total time=   0.1s
[CV 2/5; 357/400] START max_depth=9, min_samples_leaf=4, n_estimators=7.........
[CV 2/5; 357/400] END max_depth=9, min_samples_leaf=4, n_estimators=7;, score=0.847 total time=   0.1s
[CV 3/5; 357/400] START max_depth=9, min_samples_leaf=4, n_estimators=7.........
[CV 3/5; 357/400] END max_depth=9, min_samples_leaf=4, n_estimators=7;, score=0.846 total time=   0.1s
[CV 4/5; 357/400] START max_depth=9, min_samples_leaf=4, n_estimators=7.........
[CV 4/5; 357/400] END max_depth=9, min_samples_leaf=4, n_estimators=7;, score=0.846 total time=   0.1s
[CV 5/5; 357/400] START max_depth=9, min_samples_leaf=4, n_estimators=7.........
[CV 5/5; 357/400] END max_depth=9, min_samples_leaf=4, n_estimators=7;, score=0.843 total time=   0.1s
[CV 1/5; 358/400] START max_depth=9, min_samples_leaf=4, n_estimators=8.........
[CV 1/5; 358/400] END max_depth=9, min_samples_leaf=4, n_estimators=8;, score=0.857 total time=   0.1s
[CV 2/5; 358/400] START max_depth=9, min_samples_leaf=4, n_estimators=8.........
[CV 2/5; 358/400] END max_depth=9, min_samples_leaf=4, n_estimators=8;, score=0.855 total time=   0.1s
[CV 3/5; 358/400] START max_depth=9, min_samples_leaf=4, n_estimators=8.........
[CV 3/5; 358/400] END max_depth=9, min_samples_leaf=4, n_estimators=8;, score=0.854 total time=   0.1s
[CV 4/5; 358/400] START max_depth=9, min_samples_leaf=4, n_estimators=8.........
[CV 4/5; 358/400] END max_depth=9, min_samples_leaf=4, n_estimators=8;, score=0.842 total time=   0.1s
[CV 5/5; 358/400] START max_depth=9, min_samples_leaf=4, n_estimators=8.........
[CV 5/5; 358/400] END max_depth=9, min_samples_leaf=4, n_estimators=8;, score=0.837 total time=   0.1s
[CV 1/5; 359/400] START max_depth=9, min_samples_leaf=4, n_estimators=9.........
[CV 1/5; 359/400] END max_depth=9, min_samples_leaf=4, n_estimators=9;, score=0.859 total time=   0.1s
[CV 2/5; 359/400] START max_depth=9, min_samples_leaf=4, n_estimators=9.........
[CV 2/5; 359/400] END max_depth=9, min_samples_leaf=4, n_estimators=9;, score=0.855 total time=   0.1s
[CV 3/5; 359/400] START max_depth=9, min_samples_leaf=4, n_estimators=9.........
[CV 3/5; 359/400] END max_depth=9, min_samples_leaf=4, n_estimators=9;, score=0.854 total time=   0.1s
[CV 4/5; 359/400] START max_depth=9, min_samples_leaf=4, n_estimators=9.........
[CV 4/5; 359/400] END max_depth=9, min_samples_leaf=4, n_estimators=9;, score=0.850 total time=   0.1s
[CV 5/5; 359/400] START max_depth=9, min_samples_leaf=4, n_estimators=9.........
[CV 5/5; 359/400] END max_depth=9, min_samples_leaf=4, n_estimators=9;, score=0.853 total time=   0.1s
[CV 1/5; 360/400] START max_depth=9, min_samples_leaf=4, n_estimators=10........
[CV 1/5; 360/400] END max_depth=9, min_samples_leaf=4, n_estimators=10;, score=0.843 total time=   0.1s
[CV 2/5; 360/400] START max_depth=9, min_samples_leaf=4, n_estimators=10........
[CV 2/5; 360/400] END max_depth=9, min_samples_leaf=4, n_estimators=10;, score=0.863 total time=   0.1s
[CV 3/5; 360/400] START max_depth=9, min_samples_leaf=4, n_estimators=10........
[CV 3/5; 360/400] END max_depth=9, min_samples_leaf=4, n_estimators=10;, score=0.845 total time=   0.1s
[CV 4/5; 360/400] START max_depth=9, min_samples_leaf=4, n_estimators=10........
[CV 4/5; 360/400] END max_depth=9, min_samples_leaf=4, n_estimators=10;, score=0.854 total time=   0.1s
[CV 5/5; 360/400] START max_depth=9, min_samples_leaf=4, n_estimators=10........
[CV 5/5; 360/400] END max_depth=9, min_samples_leaf=4, n_estimators=10;, score=0.857 total time=   0.1s
[CV 1/5; 361/400] START max_depth=10, min_samples_leaf=1, n_estimators=1........
[CV 1/5; 361/400] END max_depth=10, min_samples_leaf=1, n_estimators=1;, score=0.811 total time=   0.0s
[CV 2/5; 361/400] START max_depth=10, min_samples_leaf=1, n_estimators=1........
[CV 2/5; 361/400] END max_depth=10, min_samples_leaf=1, n_estimators=1;, score=0.813 total time=   0.0s
[CV 3/5; 361/400] START max_depth=10, min_samples_leaf=1, n_estimators=1........
[CV 3/5; 361/400] END max_depth=10, min_samples_leaf=1, n_estimators=1;, score=0.827 total time=   0.0s
[CV 4/5; 361/400] START max_depth=10, min_samples_leaf=1, n_estimators=1........
[CV 4/5; 361/400] END max_depth=10, min_samples_leaf=1, n_estimators=1;, score=0.819 total time=   0.0s
[CV 5/5; 361/400] START max_depth=10, min_samples_leaf=1, n_estimators=1........
[CV 5/5; 361/400] END max_depth=10, min_samples_leaf=1, n_estimators=1;, score=0.805 total time=   0.0s
[CV 1/5; 362/400] START max_depth=10, min_samples_leaf=1, n_estimators=2........
[CV 1/5; 362/400] END max_depth=10, min_samples_leaf=1, n_estimators=2;, score=0.859 total time=   0.0s
[CV 2/5; 362/400] START max_depth=10, min_samples_leaf=1, n_estimators=2........
[CV 2/5; 362/400] END max_depth=10, min_samples_leaf=1, n_estimators=2;, score=0.830 total time=   0.0s
[CV 3/5; 362/400] START max_depth=10, min_samples_leaf=1, n_estimators=2........
[CV 3/5; 362/400] END max_depth=10, min_samples_leaf=1, n_estimators=2;, score=0.849 total time=   0.0s
[CV 4/5; 362/400] START max_depth=10, min_samples_leaf=1, n_estimators=2........
[CV 4/5; 362/400] END max_depth=10, min_samples_leaf=1, n_estimators=2;, score=0.831 total time=   0.0s
[CV 5/5; 362/400] START max_depth=10, min_samples_leaf=1, n_estimators=2........
[CV 5/5; 362/400] END max_depth=10, min_samples_leaf=1, n_estimators=2;, score=0.866 total time=   0.0s
[CV 1/5; 363/400] START max_depth=10, min_samples_leaf=1, n_estimators=3........
[CV 1/5; 363/400] END max_depth=10, min_samples_leaf=1, n_estimators=3;, score=0.852 total time=   0.0s
[CV 2/5; 363/400] START max_depth=10, min_samples_leaf=1, n_estimators=3........
[CV 2/5; 363/400] END max_depth=10, min_samples_leaf=1, n_estimators=3;, score=0.838 total time=   0.0s
[CV 3/5; 363/400] START max_depth=10, min_samples_leaf=1, n_estimators=3........
[CV 3/5; 363/400] END max_depth=10, min_samples_leaf=1, n_estimators=3;, score=0.852 total time=   0.0s
[CV 4/5; 363/400] START max_depth=10, min_samples_leaf=1, n_estimators=3........
[CV 4/5; 363/400] END max_depth=10, min_samples_leaf=1, n_estimators=3;, score=0.847 total time=   0.0s
[CV 5/5; 363/400] START max_depth=10, min_samples_leaf=1, n_estimators=3........
[CV 5/5; 363/400] END max_depth=10, min_samples_leaf=1, n_estimators=3;, score=0.863 total time=   0.0s
[CV 1/5; 364/400] START max_depth=10, min_samples_leaf=1, n_estimators=4........
[CV 1/5; 364/400] END max_depth=10, min_samples_leaf=1, n_estimators=4;, score=0.846 total time=   0.0s
[CV 2/5; 364/400] START max_depth=10, min_samples_leaf=1, n_estimators=4........
[CV 2/5; 364/400] END max_depth=10, min_samples_leaf=1, n_estimators=4;, score=0.855 total time=   0.0s
[CV 3/5; 364/400] START max_depth=10, min_samples_leaf=1, n_estimators=4........
[CV 3/5; 364/400] END max_depth=10, min_samples_leaf=1, n_estimators=4;, score=0.868 total time=   0.0s
[CV 4/5; 364/400] START max_depth=10, min_samples_leaf=1, n_estimators=4........
[CV 4/5; 364/400] END max_depth=10, min_samples_leaf=1, n_estimators=4;, score=0.859 total time=   0.0s
[CV 5/5; 364/400] START max_depth=10, min_samples_leaf=1, n_estimators=4........
[CV 5/5; 364/400] END max_depth=10, min_samples_leaf=1, n_estimators=4;, score=0.860 total time=   0.0s
[CV 1/5; 365/400] START max_depth=10, min_samples_leaf=1, n_estimators=5........
[CV 1/5; 365/400] END max_depth=10, min_samples_leaf=1, n_estimators=5;, score=0.852 total time=   0.0s
[CV 2/5; 365/400] START max_depth=10, min_samples_leaf=1, n_estimators=5........
[CV 2/5; 365/400] END max_depth=10, min_samples_leaf=1, n_estimators=5;, score=0.867 total time=   0.0s
[CV 3/5; 365/400] START max_depth=10, min_samples_leaf=1, n_estimators=5........
[CV 3/5; 365/400] END max_depth=10, min_samples_leaf=1, n_estimators=5;, score=0.874 total time=   0.0s
[CV 4/5; 365/400] START max_depth=10, min_samples_leaf=1, n_estimators=5........
[CV 4/5; 365/400] END max_depth=10, min_samples_leaf=1, n_estimators=5;, score=0.852 total time=   0.0s
[CV 5/5; 365/400] START max_depth=10, min_samples_leaf=1, n_estimators=5........
[CV 5/5; 365/400] END max_depth=10, min_samples_leaf=1, n_estimators=5;, score=0.863 total time=   0.0s
[CV 1/5; 366/400] START max_depth=10, min_samples_leaf=1, n_estimators=6........
[CV 1/5; 366/400] END max_depth=10, min_samples_leaf=1, n_estimators=6;, score=0.871 total time=   0.1s
[CV 2/5; 366/400] START max_depth=10, min_samples_leaf=1, n_estimators=6........
[CV 2/5; 366/400] END max_depth=10, min_samples_leaf=1, n_estimators=6;, score=0.870 total time=   0.0s
[CV 3/5; 366/400] START max_depth=10, min_samples_leaf=1, n_estimators=6........
[CV 3/5; 366/400] END max_depth=10, min_samples_leaf=1, n_estimators=6;, score=0.870 total time=   0.0s
[CV 4/5; 366/400] START max_depth=10, min_samples_leaf=1, n_estimators=6........
[CV 4/5; 366/400] END max_depth=10, min_samples_leaf=1, n_estimators=6;, score=0.871 total time=   0.0s
[CV 5/5; 366/400] START max_depth=10, min_samples_leaf=1, n_estimators=6........
[CV 5/5; 366/400] END max_depth=10, min_samples_leaf=1, n_estimators=6;, score=0.855 total time=   0.1s
[CV 1/5; 367/400] START max_depth=10, min_samples_leaf=1, n_estimators=7........
[CV 1/5; 367/400] END max_depth=10, min_samples_leaf=1, n_estimators=7;, score=0.867 total time=   0.1s
[CV 2/5; 367/400] START max_depth=10, min_samples_leaf=1, n_estimators=7........
[CV 2/5; 367/400] END max_depth=10, min_samples_leaf=1, n_estimators=7;, score=0.878 total time=   0.1s
[CV 3/5; 367/400] START max_depth=10, min_samples_leaf=1, n_estimators=7........
[CV 3/5; 367/400] END max_depth=10, min_samples_leaf=1, n_estimators=7;, score=0.871 total time=   0.1s
[CV 4/5; 367/400] START max_depth=10, min_samples_leaf=1, n_estimators=7........
[CV 4/5; 367/400] END max_depth=10, min_samples_leaf=1, n_estimators=7;, score=0.862 total time=   0.1s
[CV 5/5; 367/400] START max_depth=10, min_samples_leaf=1, n_estimators=7........
[CV 5/5; 367/400] END max_depth=10, min_samples_leaf=1, n_estimators=7;, score=0.863 total time=   0.1s
[CV 1/5; 368/400] START max_depth=10, min_samples_leaf=1, n_estimators=8........
[CV 1/5; 368/400] END max_depth=10, min_samples_leaf=1, n_estimators=8;, score=0.876 total time=   0.1s
[CV 2/5; 368/400] START max_depth=10, min_samples_leaf=1, n_estimators=8........
[CV 2/5; 368/400] END max_depth=10, min_samples_leaf=1, n_estimators=8;, score=0.866 total time=   0.1s
[CV 3/5; 368/400] START max_depth=10, min_samples_leaf=1, n_estimators=8........
[CV 3/5; 368/400] END max_depth=10, min_samples_leaf=1, n_estimators=8;, score=0.871 total time=   0.1s
[CV 4/5; 368/400] START max_depth=10, min_samples_leaf=1, n_estimators=8........
[CV 4/5; 368/400] END max_depth=10, min_samples_leaf=1, n_estimators=8;, score=0.876 total time=   0.1s
[CV 5/5; 368/400] START max_depth=10, min_samples_leaf=1, n_estimators=8........
[CV 5/5; 368/400] END max_depth=10, min_samples_leaf=1, n_estimators=8;, score=0.877 total time=   0.1s
[CV 1/5; 369/400] START max_depth=10, min_samples_leaf=1, n_estimators=9........
[CV 1/5; 369/400] END max_depth=10, min_samples_leaf=1, n_estimators=9;, score=0.875 total time=   0.1s
[CV 2/5; 369/400] START max_depth=10, min_samples_leaf=1, n_estimators=9........
[CV 2/5; 369/400] END max_depth=10, min_samples_leaf=1, n_estimators=9;, score=0.866 total time=   0.1s
[CV 3/5; 369/400] START max_depth=10, min_samples_leaf=1, n_estimators=9........
[CV 3/5; 369/400] END max_depth=10, min_samples_leaf=1, n_estimators=9;, score=0.860 total time=   0.1s
[CV 4/5; 369/400] START max_depth=10, min_samples_leaf=1, n_estimators=9........
[CV 4/5; 369/400] END max_depth=10, min_samples_leaf=1, n_estimators=9;, score=0.862 total time=   0.1s
[CV 5/5; 369/400] START max_depth=10, min_samples_leaf=1, n_estimators=9........
[CV 5/5; 369/400] END max_depth=10, min_samples_leaf=1, n_estimators=9;, score=0.873 total time=   0.1s
[CV 1/5; 370/400] START max_depth=10, min_samples_leaf=1, n_estimators=10.......
[CV 1/5; 370/400] END max_depth=10, min_samples_leaf=1, n_estimators=10;, score=0.877 total time=   0.1s
[CV 2/5; 370/400] START max_depth=10, min_samples_leaf=1, n_estimators=10.......
[CV 2/5; 370/400] END max_depth=10, min_samples_leaf=1, n_estimators=10;, score=0.866 total time=   0.1s
[CV 3/5; 370/400] START max_depth=10, min_samples_leaf=1, n_estimators=10.......
[CV 3/5; 370/400] END max_depth=10, min_samples_leaf=1, n_estimators=10;, score=0.874 total time=   0.1s
[CV 4/5; 370/400] START max_depth=10, min_samples_leaf=1, n_estimators=10.......
[CV 4/5; 370/400] END max_depth=10, min_samples_leaf=1, n_estimators=10;, score=0.874 total time=   0.1s
[CV 5/5; 370/400] START max_depth=10, min_samples_leaf=1, n_estimators=10.......
[CV 5/5; 370/400] END max_depth=10, min_samples_leaf=1, n_estimators=10;, score=0.871 total time=   0.1s
[CV 1/5; 371/400] START max_depth=10, min_samples_leaf=2, n_estimators=1........
[CV 1/5; 371/400] END max_depth=10, min_samples_leaf=2, n_estimators=1;, score=0.802 total time=   0.0s
[CV 2/5; 371/400] START max_depth=10, min_samples_leaf=2, n_estimators=1........
[CV 2/5; 371/400] END max_depth=10, min_samples_leaf=2, n_estimators=1;, score=0.811 total time=   0.0s
[CV 3/5; 371/400] START max_depth=10, min_samples_leaf=2, n_estimators=1........
[CV 3/5; 371/400] END max_depth=10, min_samples_leaf=2, n_estimators=1;, score=0.824 total time=   0.0s
[CV 4/5; 371/400] START max_depth=10, min_samples_leaf=2, n_estimators=1........
[CV 4/5; 371/400] END max_depth=10, min_samples_leaf=2, n_estimators=1;, score=0.818 total time=   0.0s
[CV 5/5; 371/400] START max_depth=10, min_samples_leaf=2, n_estimators=1........
[CV 5/5; 371/400] END max_depth=10, min_samples_leaf=2, n_estimators=1;, score=0.837 total time=   0.0s
[CV 1/5; 372/400] START max_depth=10, min_samples_leaf=2, n_estimators=2........
[CV 1/5; 372/400] END max_depth=10, min_samples_leaf=2, n_estimators=2;, score=0.850 total time=   0.0s
[CV 2/5; 372/400] START max_depth=10, min_samples_leaf=2, n_estimators=2........
[CV 2/5; 372/400] END max_depth=10, min_samples_leaf=2, n_estimators=2;, score=0.825 total time=   0.0s
[CV 3/5; 372/400] START max_depth=10, min_samples_leaf=2, n_estimators=2........
[CV 3/5; 372/400] END max_depth=10, min_samples_leaf=2, n_estimators=2;, score=0.842 total time=   0.0s
[CV 4/5; 372/400] START max_depth=10, min_samples_leaf=2, n_estimators=2........
[CV 4/5; 372/400] END max_depth=10, min_samples_leaf=2, n_estimators=2;, score=0.833 total time=   0.0s
[CV 5/5; 372/400] START max_depth=10, min_samples_leaf=2, n_estimators=2........
[CV 5/5; 372/400] END max_depth=10, min_samples_leaf=2, n_estimators=2;, score=0.843 total time=   0.0s
[CV 1/5; 373/400] START max_depth=10, min_samples_leaf=2, n_estimators=3........
[CV 1/5; 373/400] END max_depth=10, min_samples_leaf=2, n_estimators=3;, score=0.858 total time=   0.0s
[CV 2/5; 373/400] START max_depth=10, min_samples_leaf=2, n_estimators=3........
[CV 2/5; 373/400] END max_depth=10, min_samples_leaf=2, n_estimators=3;, score=0.843 total time=   0.0s
[CV 3/5; 373/400] START max_depth=10, min_samples_leaf=2, n_estimators=3........
[CV 3/5; 373/400] END max_depth=10, min_samples_leaf=2, n_estimators=3;, score=0.853 total time=   0.0s
[CV 4/5; 373/400] START max_depth=10, min_samples_leaf=2, n_estimators=3........
[CV 4/5; 373/400] END max_depth=10, min_samples_leaf=2, n_estimators=3;, score=0.843 total time=   0.0s
[CV 5/5; 373/400] START max_depth=10, min_samples_leaf=2, n_estimators=3........
[CV 5/5; 373/400] END max_depth=10, min_samples_leaf=2, n_estimators=3;, score=0.825 total time=   0.0s
[CV 1/5; 374/400] START max_depth=10, min_samples_leaf=2, n_estimators=4........
[CV 1/5; 374/400] END max_depth=10, min_samples_leaf=2, n_estimators=4;, score=0.856 total time=   0.0s
[CV 2/5; 374/400] START max_depth=10, min_samples_leaf=2, n_estimators=4........
[CV 2/5; 374/400] END max_depth=10, min_samples_leaf=2, n_estimators=4;, score=0.854 total time=   0.0s
[CV 3/5; 374/400] START max_depth=10, min_samples_leaf=2, n_estimators=4........
[CV 3/5; 374/400] END max_depth=10, min_samples_leaf=2, n_estimators=4;, score=0.860 total time=   0.0s
[CV 4/5; 374/400] START max_depth=10, min_samples_leaf=2, n_estimators=4........
[CV 4/5; 374/400] END max_depth=10, min_samples_leaf=2, n_estimators=4;, score=0.853 total time=   0.0s
[CV 5/5; 374/400] START max_depth=10, min_samples_leaf=2, n_estimators=4........
[CV 5/5; 374/400] END max_depth=10, min_samples_leaf=2, n_estimators=4;, score=0.874 total time=   0.0s
[CV 1/5; 375/400] START max_depth=10, min_samples_leaf=2, n_estimators=5........
[CV 1/5; 375/400] END max_depth=10, min_samples_leaf=2, n_estimators=5;, score=0.864 total time=   0.0s
[CV 2/5; 375/400] START max_depth=10, min_samples_leaf=2, n_estimators=5........
[CV 2/5; 375/400] END max_depth=10, min_samples_leaf=2, n_estimators=5;, score=0.865 total time=   0.0s
[CV 3/5; 375/400] START max_depth=10, min_samples_leaf=2, n_estimators=5........
[CV 3/5; 375/400] END max_depth=10, min_samples_leaf=2, n_estimators=5;, score=0.861 total time=   0.0s
[CV 4/5; 375/400] START max_depth=10, min_samples_leaf=2, n_estimators=5........
[CV 4/5; 375/400] END max_depth=10, min_samples_leaf=2, n_estimators=5;, score=0.865 total time=   0.0s
[CV 5/5; 375/400] START max_depth=10, min_samples_leaf=2, n_estimators=5........
[CV 5/5; 375/400] END max_depth=10, min_samples_leaf=2, n_estimators=5;, score=0.856 total time=   0.0s
[CV 1/5; 376/400] START max_depth=10, min_samples_leaf=2, n_estimators=6........
[CV 1/5; 376/400] END max_depth=10, min_samples_leaf=2, n_estimators=6;, score=0.863 total time=   0.0s
[CV 2/5; 376/400] START max_depth=10, min_samples_leaf=2, n_estimators=6........
[CV 2/5; 376/400] END max_depth=10, min_samples_leaf=2, n_estimators=6;, score=0.860 total time=   0.0s
[CV 3/5; 376/400] START max_depth=10, min_samples_leaf=2, n_estimators=6........
[CV 3/5; 376/400] END max_depth=10, min_samples_leaf=2, n_estimators=6;, score=0.860 total time=   0.1s
[CV 4/5; 376/400] START max_depth=10, min_samples_leaf=2, n_estimators=6........
[CV 4/5; 376/400] END max_depth=10, min_samples_leaf=2, n_estimators=6;, score=0.863 total time=   0.1s
[CV 5/5; 376/400] START max_depth=10, min_samples_leaf=2, n_estimators=6........
[CV 5/5; 376/400] END max_depth=10, min_samples_leaf=2, n_estimators=6;, score=0.870 total time=   0.1s
[CV 1/5; 377/400] START max_depth=10, min_samples_leaf=2, n_estimators=7........
[CV 1/5; 377/400] END max_depth=10, min_samples_leaf=2, n_estimators=7;, score=0.876 total time=   0.1s
[CV 2/5; 377/400] START max_depth=10, min_samples_leaf=2, n_estimators=7........
[CV 2/5; 377/400] END max_depth=10, min_samples_leaf=2, n_estimators=7;, score=0.864 total time=   0.1s
[CV 3/5; 377/400] START max_depth=10, min_samples_leaf=2, n_estimators=7........
[CV 3/5; 377/400] END max_depth=10, min_samples_leaf=2, n_estimators=7;, score=0.874 total time=   0.1s
[CV 4/5; 377/400] START max_depth=10, min_samples_leaf=2, n_estimators=7........
[CV 4/5; 377/400] END max_depth=10, min_samples_leaf=2, n_estimators=7;, score=0.871 total time=   0.1s
[CV 5/5; 377/400] START max_depth=10, min_samples_leaf=2, n_estimators=7........
[CV 5/5; 377/400] END max_depth=10, min_samples_leaf=2, n_estimators=7;, score=0.860 total time=   0.1s
[CV 1/5; 378/400] START max_depth=10, min_samples_leaf=2, n_estimators=8........
[CV 1/5; 378/400] END max_depth=10, min_samples_leaf=2, n_estimators=8;, score=0.874 total time=   0.1s
[CV 2/5; 378/400] START max_depth=10, min_samples_leaf=2, n_estimators=8........
[CV 2/5; 378/400] END max_depth=10, min_samples_leaf=2, n_estimators=8;, score=0.861 total time=   0.1s
[CV 3/5; 378/400] START max_depth=10, min_samples_leaf=2, n_estimators=8........
[CV 3/5; 378/400] END max_depth=10, min_samples_leaf=2, n_estimators=8;, score=0.864 total time=   0.1s
[CV 4/5; 378/400] START max_depth=10, min_samples_leaf=2, n_estimators=8........
[CV 4/5; 378/400] END max_depth=10, min_samples_leaf=2, n_estimators=8;, score=0.867 total time=   0.1s
[CV 5/5; 378/400] START max_depth=10, min_samples_leaf=2, n_estimators=8........
[CV 5/5; 378/400] END max_depth=10, min_samples_leaf=2, n_estimators=8;, score=0.870 total time=   0.1s
[CV 1/5; 379/400] START max_depth=10, min_samples_leaf=2, n_estimators=9........
[CV 1/5; 379/400] END max_depth=10, min_samples_leaf=2, n_estimators=9;, score=0.869 total time=   0.1s
[CV 2/5; 379/400] START max_depth=10, min_samples_leaf=2, n_estimators=9........
[CV 2/5; 379/400] END max_depth=10, min_samples_leaf=2, n_estimators=9;, score=0.871 total time=   0.1s
[CV 3/5; 379/400] START max_depth=10, min_samples_leaf=2, n_estimators=9........
[CV 3/5; 379/400] END max_depth=10, min_samples_leaf=2, n_estimators=9;, score=0.877 total time=   0.1s
[CV 4/5; 379/400] START max_depth=10, min_samples_leaf=2, n_estimators=9........
[CV 4/5; 379/400] END max_depth=10, min_samples_leaf=2, n_estimators=9;, score=0.862 total time=   0.1s
[CV 5/5; 379/400] START max_depth=10, min_samples_leaf=2, n_estimators=9........
[CV 5/5; 379/400] END max_depth=10, min_samples_leaf=2, n_estimators=9;, score=0.864 total time=   0.1s
[CV 1/5; 380/400] START max_depth=10, min_samples_leaf=2, n_estimators=10.......
[CV 1/5; 380/400] END max_depth=10, min_samples_leaf=2, n_estimators=10;, score=0.864 total time=   0.1s
[CV 2/5; 380/400] START max_depth=10, min_samples_leaf=2, n_estimators=10.......
[CV 2/5; 380/400] END max_depth=10, min_samples_leaf=2, n_estimators=10;, score=0.874 total time=   0.1s
[CV 3/5; 380/400] START max_depth=10, min_samples_leaf=2, n_estimators=10.......
[CV 3/5; 380/400] END max_depth=10, min_samples_leaf=2, n_estimators=10;, score=0.870 total time=   0.1s
[CV 4/5; 380/400] START max_depth=10, min_samples_leaf=2, n_estimators=10.......
[CV 4/5; 380/400] END max_depth=10, min_samples_leaf=2, n_estimators=10;, score=0.875 total time=   0.1s
[CV 5/5; 380/400] START max_depth=10, min_samples_leaf=2, n_estimators=10.......
[CV 5/5; 380/400] END max_depth=10, min_samples_leaf=2, n_estimators=10;, score=0.863 total time=   0.1s
[CV 1/5; 381/400] START max_depth=10, min_samples_leaf=3, n_estimators=1........
[CV 1/5; 381/400] END max_depth=10, min_samples_leaf=3, n_estimators=1;, score=0.808 total time=   0.0s
[CV 2/5; 381/400] START max_depth=10, min_samples_leaf=3, n_estimators=1........
[CV 2/5; 381/400] END max_depth=10, min_samples_leaf=3, n_estimators=1;, score=0.819 total time=   0.0s
[CV 3/5; 381/400] START max_depth=10, min_samples_leaf=3, n_estimators=1........
[CV 3/5; 381/400] END max_depth=10, min_samples_leaf=3, n_estimators=1;, score=0.825 total time=   0.0s
[CV 4/5; 381/400] START max_depth=10, min_samples_leaf=3, n_estimators=1........
[CV 4/5; 381/400] END max_depth=10, min_samples_leaf=3, n_estimators=1;, score=0.794 total time=   0.0s
[CV 5/5; 381/400] START max_depth=10, min_samples_leaf=3, n_estimators=1........
[CV 5/5; 381/400] END max_depth=10, min_samples_leaf=3, n_estimators=1;, score=0.810 total time=   0.0s
[CV 1/5; 382/400] START max_depth=10, min_samples_leaf=3, n_estimators=2........
[CV 1/5; 382/400] END max_depth=10, min_samples_leaf=3, n_estimators=2;, score=0.850 total time=   0.0s
[CV 2/5; 382/400] START max_depth=10, min_samples_leaf=3, n_estimators=2........
[CV 2/5; 382/400] END max_depth=10, min_samples_leaf=3, n_estimators=2;, score=0.821 total time=   0.0s
[CV 3/5; 382/400] START max_depth=10, min_samples_leaf=3, n_estimators=2........
[CV 3/5; 382/400] END max_depth=10, min_samples_leaf=3, n_estimators=2;, score=0.833 total time=   0.0s
[CV 4/5; 382/400] START max_depth=10, min_samples_leaf=3, n_estimators=2........
[CV 4/5; 382/400] END max_depth=10, min_samples_leaf=3, n_estimators=2;, score=0.838 total time=   0.0s
[CV 5/5; 382/400] START max_depth=10, min_samples_leaf=3, n_estimators=2........
[CV 5/5; 382/400] END max_depth=10, min_samples_leaf=3, n_estimators=2;, score=0.829 total time=   0.0s
[CV 1/5; 383/400] START max_depth=10, min_samples_leaf=3, n_estimators=3........
[CV 1/5; 383/400] END max_depth=10, min_samples_leaf=3, n_estimators=3;, score=0.858 total time=   0.0s
[CV 2/5; 383/400] START max_depth=10, min_samples_leaf=3, n_estimators=3........
[CV 2/5; 383/400] END max_depth=10, min_samples_leaf=3, n_estimators=3;, score=0.848 total time=   0.0s
[CV 3/5; 383/400] START max_depth=10, min_samples_leaf=3, n_estimators=3........
[CV 3/5; 383/400] END max_depth=10, min_samples_leaf=3, n_estimators=3;, score=0.838 total time=   0.0s
[CV 4/5; 383/400] START max_depth=10, min_samples_leaf=3, n_estimators=3........
[CV 4/5; 383/400] END max_depth=10, min_samples_leaf=3, n_estimators=3;, score=0.851 total time=   0.0s
[CV 5/5; 383/400] START max_depth=10, min_samples_leaf=3, n_estimators=3........
[CV 5/5; 383/400] END max_depth=10, min_samples_leaf=3, n_estimators=3;, score=0.860 total time=   0.0s
[CV 1/5; 384/400] START max_depth=10, min_samples_leaf=3, n_estimators=4........
[CV 1/5; 384/400] END max_depth=10, min_samples_leaf=3, n_estimators=4;, score=0.863 total time=   0.0s
[CV 2/5; 384/400] START max_depth=10, min_samples_leaf=3, n_estimators=4........
[CV 2/5; 384/400] END max_depth=10, min_samples_leaf=3, n_estimators=4;, score=0.855 total time=   0.0s
[CV 3/5; 384/400] START max_depth=10, min_samples_leaf=3, n_estimators=4........
[CV 3/5; 384/400] END max_depth=10, min_samples_leaf=3, n_estimators=4;, score=0.861 total time=   0.0s
[CV 4/5; 384/400] START max_depth=10, min_samples_leaf=3, n_estimators=4........
[CV 4/5; 384/400] END max_depth=10, min_samples_leaf=3, n_estimators=4;, score=0.852 total time=   0.0s
[CV 5/5; 384/400] START max_depth=10, min_samples_leaf=3, n_estimators=4........
[CV 5/5; 384/400] END max_depth=10, min_samples_leaf=3, n_estimators=4;, score=0.871 total time=   0.0s
[CV 1/5; 385/400] START max_depth=10, min_samples_leaf=3, n_estimators=5........
[CV 1/5; 385/400] END max_depth=10, min_samples_leaf=3, n_estimators=5;, score=0.857 total time=   0.0s
[CV 2/5; 385/400] START max_depth=10, min_samples_leaf=3, n_estimators=5........
[CV 2/5; 385/400] END max_depth=10, min_samples_leaf=3, n_estimators=5;, score=0.846 total time=   0.0s
[CV 3/5; 385/400] START max_depth=10, min_samples_leaf=3, n_estimators=5........
[CV 3/5; 385/400] END max_depth=10, min_samples_leaf=3, n_estimators=5;, score=0.844 total time=   0.0s
[CV 4/5; 385/400] START max_depth=10, min_samples_leaf=3, n_estimators=5........
[CV 4/5; 385/400] END max_depth=10, min_samples_leaf=3, n_estimators=5;, score=0.866 total time=   0.0s
[CV 5/5; 385/400] START max_depth=10, min_samples_leaf=3, n_estimators=5........
[CV 5/5; 385/400] END max_depth=10, min_samples_leaf=3, n_estimators=5;, score=0.862 total time=   0.1s
[CV 1/5; 386/400] START max_depth=10, min_samples_leaf=3, n_estimators=6........
[CV 1/5; 386/400] END max_depth=10, min_samples_leaf=3, n_estimators=6;, score=0.876 total time=   0.0s
[CV 2/5; 386/400] START max_depth=10, min_samples_leaf=3, n_estimators=6........
[CV 2/5; 386/400] END max_depth=10, min_samples_leaf=3, n_estimators=6;, score=0.855 total time=   0.0s
[CV 3/5; 386/400] START max_depth=10, min_samples_leaf=3, n_estimators=6........
[CV 3/5; 386/400] END max_depth=10, min_samples_leaf=3, n_estimators=6;, score=0.863 total time=   0.0s
[CV 4/5; 386/400] START max_depth=10, min_samples_leaf=3, n_estimators=6........
[CV 4/5; 386/400] END max_depth=10, min_samples_leaf=3, n_estimators=6;, score=0.864 total time=   0.1s
[CV 5/5; 386/400] START max_depth=10, min_samples_leaf=3, n_estimators=6........
[CV 5/5; 386/400] END max_depth=10, min_samples_leaf=3, n_estimators=6;, score=0.855 total time=   0.0s
[CV 1/5; 387/400] START max_depth=10, min_samples_leaf=3, n_estimators=7........
[CV 1/5; 387/400] END max_depth=10, min_samples_leaf=3, n_estimators=7;, score=0.856 total time=   0.1s
[CV 2/5; 387/400] START max_depth=10, min_samples_leaf=3, n_estimators=7........
[CV 2/5; 387/400] END max_depth=10, min_samples_leaf=3, n_estimators=7;, score=0.859 total time=   0.1s
[CV 3/5; 387/400] START max_depth=10, min_samples_leaf=3, n_estimators=7........
[CV 3/5; 387/400] END max_depth=10, min_samples_leaf=3, n_estimators=7;, score=0.855 total time=   0.1s
[CV 4/5; 387/400] START max_depth=10, min_samples_leaf=3, n_estimators=7........
[CV 4/5; 387/400] END max_depth=10, min_samples_leaf=3, n_estimators=7;, score=0.859 total time=   0.1s
[CV 5/5; 387/400] START max_depth=10, min_samples_leaf=3, n_estimators=7........
[CV 5/5; 387/400] END max_depth=10, min_samples_leaf=3, n_estimators=7;, score=0.848 total time=   0.1s
[CV 1/5; 388/400] START max_depth=10, min_samples_leaf=3, n_estimators=8........
[CV 1/5; 388/400] END max_depth=10, min_samples_leaf=3, n_estimators=8;, score=0.869 total time=   0.1s
[CV 2/5; 388/400] START max_depth=10, min_samples_leaf=3, n_estimators=8........
[CV 2/5; 388/400] END max_depth=10, min_samples_leaf=3, n_estimators=8;, score=0.865 total time=   0.1s
[CV 3/5; 388/400] START max_depth=10, min_samples_leaf=3, n_estimators=8........
[CV 3/5; 388/400] END max_depth=10, min_samples_leaf=3, n_estimators=8;, score=0.874 total time=   0.1s
[CV 4/5; 388/400] START max_depth=10, min_samples_leaf=3, n_estimators=8........
[CV 4/5; 388/400] END max_depth=10, min_samples_leaf=3, n_estimators=8;, score=0.867 total time=   0.1s
[CV 5/5; 388/400] START max_depth=10, min_samples_leaf=3, n_estimators=8........
[CV 5/5; 388/400] END max_depth=10, min_samples_leaf=3, n_estimators=8;, score=0.872 total time=   0.1s
[CV 1/5; 389/400] START max_depth=10, min_samples_leaf=3, n_estimators=9........
[CV 1/5; 389/400] END max_depth=10, min_samples_leaf=3, n_estimators=9;, score=0.863 total time=   0.1s
[CV 2/5; 389/400] START max_depth=10, min_samples_leaf=3, n_estimators=9........
[CV 2/5; 389/400] END max_depth=10, min_samples_leaf=3, n_estimators=9;, score=0.857 total time=   0.1s
[CV 3/5; 389/400] START max_depth=10, min_samples_leaf=3, n_estimators=9........
[CV 3/5; 389/400] END max_depth=10, min_samples_leaf=3, n_estimators=9;, score=0.863 total time=   0.1s
[CV 4/5; 389/400] START max_depth=10, min_samples_leaf=3, n_estimators=9........
[CV 4/5; 389/400] END max_depth=10, min_samples_leaf=3, n_estimators=9;, score=0.861 total time=   0.1s
[CV 5/5; 389/400] START max_depth=10, min_samples_leaf=3, n_estimators=9........
[CV 5/5; 389/400] END max_depth=10, min_samples_leaf=3, n_estimators=9;, score=0.858 total time=   0.1s
[CV 1/5; 390/400] START max_depth=10, min_samples_leaf=3, n_estimators=10.......
[CV 1/5; 390/400] END max_depth=10, min_samples_leaf=3, n_estimators=10;, score=0.871 total time=   0.1s
[CV 2/5; 390/400] START max_depth=10, min_samples_leaf=3, n_estimators=10.......
[CV 2/5; 390/400] END max_depth=10, min_samples_leaf=3, n_estimators=10;, score=0.858 total time=   0.1s
[CV 3/5; 390/400] START max_depth=10, min_samples_leaf=3, n_estimators=10.......
[CV 3/5; 390/400] END max_depth=10, min_samples_leaf=3, n_estimators=10;, score=0.866 total time=   0.1s
[CV 4/5; 390/400] START max_depth=10, min_samples_leaf=3, n_estimators=10.......
[CV 4/5; 390/400] END max_depth=10, min_samples_leaf=3, n_estimators=10;, score=0.855 total time=   0.1s
[CV 5/5; 390/400] START max_depth=10, min_samples_leaf=3, n_estimators=10.......
[CV 5/5; 390/400] END max_depth=10, min_samples_leaf=3, n_estimators=10;, score=0.862 total time=   0.1s
[CV 1/5; 391/400] START max_depth=10, min_samples_leaf=4, n_estimators=1........
[CV 1/5; 391/400] END max_depth=10, min_samples_leaf=4, n_estimators=1;, score=0.805 total time=   0.0s
[CV 2/5; 391/400] START max_depth=10, min_samples_leaf=4, n_estimators=1........
[CV 2/5; 391/400] END max_depth=10, min_samples_leaf=4, n_estimators=1;, score=0.805 total time=   0.0s
[CV 3/5; 391/400] START max_depth=10, min_samples_leaf=4, n_estimators=1........
[CV 3/5; 391/400] END max_depth=10, min_samples_leaf=4, n_estimators=1;, score=0.793 total time=   0.0s
[CV 4/5; 391/400] START max_depth=10, min_samples_leaf=4, n_estimators=1........
[CV 4/5; 391/400] END max_depth=10, min_samples_leaf=4, n_estimators=1;, score=0.812 total time=   0.0s
[CV 5/5; 391/400] START max_depth=10, min_samples_leaf=4, n_estimators=1........
[CV 5/5; 391/400] END max_depth=10, min_samples_leaf=4, n_estimators=1;, score=0.818 total time=   0.0s
[CV 1/5; 392/400] START max_depth=10, min_samples_leaf=4, n_estimators=2........
[CV 1/5; 392/400] END max_depth=10, min_samples_leaf=4, n_estimators=2;, score=0.829 total time=   0.0s
[CV 2/5; 392/400] START max_depth=10, min_samples_leaf=4, n_estimators=2........
[CV 2/5; 392/400] END max_depth=10, min_samples_leaf=4, n_estimators=2;, score=0.831 total time=   0.0s
[CV 3/5; 392/400] START max_depth=10, min_samples_leaf=4, n_estimators=2........
[CV 3/5; 392/400] END max_depth=10, min_samples_leaf=4, n_estimators=2;, score=0.839 total time=   0.0s
[CV 4/5; 392/400] START max_depth=10, min_samples_leaf=4, n_estimators=2........
[CV 4/5; 392/400] END max_depth=10, min_samples_leaf=4, n_estimators=2;, score=0.834 total time=   0.0s
[CV 5/5; 392/400] START max_depth=10, min_samples_leaf=4, n_estimators=2........
[CV 5/5; 392/400] END max_depth=10, min_samples_leaf=4, n_estimators=2;, score=0.830 total time=   0.0s
[CV 1/5; 393/400] START max_depth=10, min_samples_leaf=4, n_estimators=3........
[CV 1/5; 393/400] END max_depth=10, min_samples_leaf=4, n_estimators=3;, score=0.838 total time=   0.0s
[CV 2/5; 393/400] START max_depth=10, min_samples_leaf=4, n_estimators=3........
[CV 2/5; 393/400] END max_depth=10, min_samples_leaf=4, n_estimators=3;, score=0.855 total time=   0.0s
[CV 3/5; 393/400] START max_depth=10, min_samples_leaf=4, n_estimators=3........
[CV 3/5; 393/400] END max_depth=10, min_samples_leaf=4, n_estimators=3;, score=0.843 total time=   0.0s
[CV 4/5; 393/400] START max_depth=10, min_samples_leaf=4, n_estimators=3........
[CV 4/5; 393/400] END max_depth=10, min_samples_leaf=4, n_estimators=3;, score=0.836 total time=   0.0s
[CV 5/5; 393/400] START max_depth=10, min_samples_leaf=4, n_estimators=3........
[CV 5/5; 393/400] END max_depth=10, min_samples_leaf=4, n_estimators=3;, score=0.841 total time=   0.1s
[CV 1/5; 394/400] START max_depth=10, min_samples_leaf=4, n_estimators=4........
[CV 1/5; 394/400] END max_depth=10, min_samples_leaf=4, n_estimators=4;, score=0.859 total time=   0.2s
[CV 2/5; 394/400] START max_depth=10, min_samples_leaf=4, n_estimators=4........
[CV 2/5; 394/400] END max_depth=10, min_samples_leaf=4, n_estimators=4;, score=0.849 total time=   0.2s
[CV 3/5; 394/400] START max_depth=10, min_samples_leaf=4, n_estimators=4........
[CV 3/5; 394/400] END max_depth=10, min_samples_leaf=4, n_estimators=4;, score=0.853 total time=   0.1s
[CV 4/5; 394/400] START max_depth=10, min_samples_leaf=4, n_estimators=4........
[CV 4/5; 394/400] END max_depth=10, min_samples_leaf=4, n_estimators=4;, score=0.851 total time=   0.1s
[CV 5/5; 394/400] START max_depth=10, min_samples_leaf=4, n_estimators=4........
[CV 5/5; 394/400] END max_depth=10, min_samples_leaf=4, n_estimators=4;, score=0.857 total time=   0.1s
[CV 1/5; 395/400] START max_depth=10, min_samples_leaf=4, n_estimators=5........
[CV 1/5; 395/400] END max_depth=10, min_samples_leaf=4, n_estimators=5;, score=0.860 total time=   0.1s
[CV 2/5; 395/400] START max_depth=10, min_samples_leaf=4, n_estimators=5........
[CV 2/5; 395/400] END max_depth=10, min_samples_leaf=4, n_estimators=5;, score=0.857 total time=   0.1s
[CV 3/5; 395/400] START max_depth=10, min_samples_leaf=4, n_estimators=5........
[CV 3/5; 395/400] END max_depth=10, min_samples_leaf=4, n_estimators=5;, score=0.858 total time=   0.1s
[CV 4/5; 395/400] START max_depth=10, min_samples_leaf=4, n_estimators=5........
[CV 4/5; 395/400] END max_depth=10, min_samples_leaf=4, n_estimators=5;, score=0.869 total time=   0.0s
[CV 5/5; 395/400] START max_depth=10, min_samples_leaf=4, n_estimators=5........
[CV 5/5; 395/400] END max_depth=10, min_samples_leaf=4, n_estimators=5;, score=0.859 total time=   0.0s
[CV 1/5; 396/400] START max_depth=10, min_samples_leaf=4, n_estimators=6........
[CV 1/5; 396/400] END max_depth=10, min_samples_leaf=4, n_estimators=6;, score=0.861 total time=   0.0s
[CV 2/5; 396/400] START max_depth=10, min_samples_leaf=4, n_estimators=6........
[CV 2/5; 396/400] END max_depth=10, min_samples_leaf=4, n_estimators=6;, score=0.854 total time=   0.0s
[CV 3/5; 396/400] START max_depth=10, min_samples_leaf=4, n_estimators=6........
[CV 3/5; 396/400] END max_depth=10, min_samples_leaf=4, n_estimators=6;, score=0.863 total time=   0.1s
[CV 4/5; 396/400] START max_depth=10, min_samples_leaf=4, n_estimators=6........
[CV 4/5; 396/400] END max_depth=10, min_samples_leaf=4, n_estimators=6;, score=0.859 total time=   0.0s
[CV 5/5; 396/400] START max_depth=10, min_samples_leaf=4, n_estimators=6........
[CV 5/5; 396/400] END max_depth=10, min_samples_leaf=4, n_estimators=6;, score=0.859 total time=   0.0s
[CV 1/5; 397/400] START max_depth=10, min_samples_leaf=4, n_estimators=7........
[CV 1/5; 397/400] END max_depth=10, min_samples_leaf=4, n_estimators=7;, score=0.860 total time=   0.1s
[CV 2/5; 397/400] START max_depth=10, min_samples_leaf=4, n_estimators=7........
[CV 2/5; 397/400] END max_depth=10, min_samples_leaf=4, n_estimators=7;, score=0.863 total time=   0.1s
[CV 3/5; 397/400] START max_depth=10, min_samples_leaf=4, n_estimators=7........
[CV 3/5; 397/400] END max_depth=10, min_samples_leaf=4, n_estimators=7;, score=0.855 total time=   0.1s
[CV 4/5; 397/400] START max_depth=10, min_samples_leaf=4, n_estimators=7........
[CV 4/5; 397/400] END max_depth=10, min_samples_leaf=4, n_estimators=7;, score=0.863 total time=   0.1s
[CV 5/5; 397/400] START max_depth=10, min_samples_leaf=4, n_estimators=7........
[CV 5/5; 397/400] END max_depth=10, min_samples_leaf=4, n_estimators=7;, score=0.864 total time=   0.1s
[CV 1/5; 398/400] START max_depth=10, min_samples_leaf=4, n_estimators=8........
[CV 1/5; 398/400] END max_depth=10, min_samples_leaf=4, n_estimators=8;, score=0.863 total time=   0.1s
[CV 2/5; 398/400] START max_depth=10, min_samples_leaf=4, n_estimators=8........
[CV 2/5; 398/400] END max_depth=10, min_samples_leaf=4, n_estimators=8;, score=0.857 total time=   0.1s
[CV 3/5; 398/400] START max_depth=10, min_samples_leaf=4, n_estimators=8........
[CV 3/5; 398/400] END max_depth=10, min_samples_leaf=4, n_estimators=8;, score=0.858 total time=   0.1s
[CV 4/5; 398/400] START max_depth=10, min_samples_leaf=4, n_estimators=8........
[CV 4/5; 398/400] END max_depth=10, min_samples_leaf=4, n_estimators=8;, score=0.859 total time=   0.1s
[CV 5/5; 398/400] START max_depth=10, min_samples_leaf=4, n_estimators=8........
[CV 5/5; 398/400] END max_depth=10, min_samples_leaf=4, n_estimators=8;, score=0.855 total time=   0.1s
[CV 1/5; 399/400] START max_depth=10, min_samples_leaf=4, n_estimators=9........
[CV 1/5; 399/400] END max_depth=10, min_samples_leaf=4, n_estimators=9;, score=0.863 total time=   0.1s
[CV 2/5; 399/400] START max_depth=10, min_samples_leaf=4, n_estimators=9........
[CV 2/5; 399/400] END max_depth=10, min_samples_leaf=4, n_estimators=9;, score=0.862 total time=   0.1s
[CV 3/5; 399/400] START max_depth=10, min_samples_leaf=4, n_estimators=9........
[CV 3/5; 399/400] END max_depth=10, min_samples_leaf=4, n_estimators=9;, score=0.860 total time=   0.1s
[CV 4/5; 399/400] START max_depth=10, min_samples_leaf=4, n_estimators=9........
[CV 4/5; 399/400] END max_depth=10, min_samples_leaf=4, n_estimators=9;, score=0.852 total time=   0.1s
[CV 5/5; 399/400] START max_depth=10, min_samples_leaf=4, n_estimators=9........
[CV 5/5; 399/400] END max_depth=10, min_samples_leaf=4, n_estimators=9;, score=0.868 total time=   0.1s
[CV 1/5; 400/400] START max_depth=10, min_samples_leaf=4, n_estimators=10.......
[CV 1/5; 400/400] END max_depth=10, min_samples_leaf=4, n_estimators=10;, score=0.863 total time=   0.1s
[CV 2/5; 400/400] START max_depth=10, min_samples_leaf=4, n_estimators=10.......
[CV 2/5; 400/400] END max_depth=10, min_samples_leaf=4, n_estimators=10;, score=0.855 total time=   0.1s
[CV 3/5; 400/400] START max_depth=10, min_samples_leaf=4, n_estimators=10.......
[CV 3/5; 400/400] END max_depth=10, min_samples_leaf=4, n_estimators=10;, score=0.875 total time=   0.1s
[CV 4/5; 400/400] START max_depth=10, min_samples_leaf=4, n_estimators=10.......
[CV 4/5; 400/400] END max_depth=10, min_samples_leaf=4, n_estimators=10;, score=0.869 total time=   0.1s
[CV 5/5; 400/400] START max_depth=10, min_samples_leaf=4, n_estimators=10.......
[CV 5/5; 400/400] END max_depth=10, min_samples_leaf=4, n_estimators=10;, score=0.858 total time=   0.1s
VOILA LES MEILLEURS PARAMÉTRES : {'max_depth': 10, 'min_samples_leaf': 1, 'n_estimators': 8}
Out[ ]:
RandomForestClassifier(max_depth=10, n_estimators=8)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RandomForestClassifier(max_depth=10, n_estimators=8)

Prédiction¶

In [ ]:
 # Prédiction sur la base test
best_Y_test_pred_RF = best_modele_RF.predict(X_test)
best_Y_test_pred_RF
Out[ ]:
array([1, 0, 0, ..., 0, 0, 1])
In [ ]:
 # Prédiction sur la base train
best_Y_train_pred_RF = best_modele_RF.predict(X_train)
best_Y_train_pred_RF
Out[ ]:
array([1, 0, 1, ..., 1, 0, 1])
In [ ]:
# prediction en proba sur la base train
Y_pred_train_proba_RF = best_modele_RF.predict_proba(X_train)
Y_pred_train_proba_RF
Out[ ]:
array([[0.1396048 , 0.8603952 ],
       [0.52972976, 0.47027024],
       [0.17221296, 0.82778704],
       ...,
       [0.20066609, 0.79933391],
       [0.99781386, 0.00218614],
       [0.24464578, 0.75535422]])

Mesures de performance¶

Accuracy¶

In [ ]:
# Sur la base test
Acc_test_RF = accuracy_score(Y_test, best_Y_test_pred_RF) * 100
Acc_train_RF = accuracy_score(Y_train, best_Y_train_pred_RF) * 100
# Sur la base train
print("Accuracy sur train:", Acc_train_RF, "%")
print("Accuracy sur test:", Acc_test_RF, "%")

Accuracy sur train: 90.58045554739162 %
Accuracy sur test: 87.00719917723688 %

F1 score¶

In [ ]:
# Sur la base test et train
F1_score_test_RF = f1_score(Y_test, best_Y_test_pred_RF, average='weighted') * 100
F1_score_train_RF = f1_score(Y_train, best_Y_train_pred_RF, average='weighted') * 100
# Sur la base train
print("F1_score sur train:", F1_score_train_RF, "%")
print("F1_score sur test:", F1_score_test_RF, "%")

F1_score sur train: 90.54154506034475 %
F1_score sur test: 86.95568107455549 %

Précision¶

In [ ]:
# Sur la base test et train
precision_score_test_RF = precision_score(Y_test, best_Y_test_pred_RF, average='weighted') * 100
precision_score_train_RF = precision_score(Y_train, best_Y_train_pred_RF, average='weighted') * 100
# Sur la base train
print("Précision sur train:", precision_score_train_RF, "%")
print("Précision sur test:", precision_score_test_RF, "%")

Précision sur train: 91.23370081323257 %
Précision sur test: 87.66095562937005 %

Recall¶

In [ ]:
# Sur la base test et train
recall_score_test_RF = recall_score(Y_test, best_Y_test_pred_RF, average='weighted') * 100
recall_score_train_RF = recall_score(Y_train, best_Y_train_pred_RF, average='weighted') * 100
# Sur la base train
print("Recall sur train:", recall_score_train_RF, "%")
print("Recall sur test:", recall_score_test_RF, "%")

Recall sur train: 90.58045554739162 %
Recall sur test: 87.00719917723688 %

Matrice de confusion¶

In [ ]:
matrice_RF = confusion_matrix(Y_test, best_Y_test_pred_RF)
# Affichage de la matrice de confusion avec titre
fig, ax = plot_confusion_matrix(conf_mat=matrice_RF,
                                show_absolute=True,
                                show_normed=True,
                                colorbar=True)

plt.title("Confusion Matrix RF")
plt.show()
print("Alors dans la classe 0,sur",Y_test.value_counts()[0],
      "individu,le modèle réussit à faire un bon classement sur", matrice_RF[0,0],
      " individu et une erreur sur", matrice_RF[0,1], "\n  Dans la classe 1,sur",Y_test.value_counts()[1],
      "individus le modèle fait un bon classement sur", matrice_RF[1,1],
      " individu et une erreur sur", matrice_RF[0,0] )
No description has been provided for this image 
Alors dans la classe 0,sur 1466 individu,le modèle réussit à faire un bon classement sur 1181  individu et une erreur sur 285 
  Dans la classe 1,sur 1451 individus le modèle fait un bon classement sur 1357  individu et une erreur sur 1181

*taux de bon et de mauvais classement*

In [ ]:
print(f"Taux de bon classement (Classe 0 - Sans AVC): {matrice_RF[0, 0] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 0 - Sans AVC): {matrice_RF[0, 1] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de bon classement (Classe 1 - Avec AVC): {matrice_RF[1, 1] / Y_test.value_counts()[1] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 1 - Avec AVC): {matrice_RF[1, 0] / Y_test.value_counts()[1] * 100:.2f} %")

Taux de bon classement (Classe 0 - Sans AVC): 80.56 %
Taux de mauvais classement (Classe 0 - Sans AVC): 19.44 %
Taux de bon classement (Classe 1 - Avec AVC): 93.52 %
Taux de mauvais classement (Classe 1 - Avec AVC): 6.48 %
In [ ]:
# Courbe ROC
fpr_RF, tpr_RF, thresholds_RF = roc_curve(Y_test, best_modele_RF.predict_proba(X_test)[:, 1])
roc_auc_RF = auc(fpr_RF, tpr_RF)

plt.figure()
plt.plot(fpr_RF, tpr_RF, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc_RF)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('Taux de faux positif')
plt.ylabel('Taux de vrai positif')
plt.title('Courbe de  ROC pour Random forest')
plt.legend(loc="lower right")
plt.show()
No description has been provided for this image
In [ ]:
result = permutation_importance(best_modele_RF, X, Y, n_repeats=10)
sorted_idx = result.importances_mean.argsort()

plt.barh(X.columns[sorted_idx], result.importances_mean[sorted_idx])
plt.xlabel("Importance des caractéristiques")
plt.show()
No description has been provided for this image

SupportVectorMachine(SVM)¶

Optimisation des paramètres¶

In [ ]:
# initialisation du modele
modele_SVC = SVC(probability=True)
# optimisation des parametres
param_grid_SVC  = ({'C' : [0.1, 1, 10, 100],
                  'kernel' : ['rbf'],
                  'gamma' : ['scale', 0.01, 0.1, 1]})
# modele optimal
modele_opt_SVC = GridSearchCV(modele_SVC, param_grid_SVC,
                            cv = 5
                            )
## Entrainement du modèle
modele_opt_SVC.fit(X_train,Y_train)
# parametre optimaux
best_param = modele_opt_SVC.best_params_
print("VOILA LES MEILLEURS PARAMÉTRES :",best_param)
# best modele
best_modele_SVC = modele_opt_SVC.best_estimator_
best_modele_SVC

VOILA LES MEILLEURS PARAMÉTRES : {'C': 100, 'gamma': 1, 'kernel': 'rbf'}
Out[ ]:
SVC(C=100, gamma=1, probability=True)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
SVC(C=100, gamma=1, probability=True)

Prédiction¶

In [ ]:
# Prédiction sur la base test
best_Y_test_pred_SVC = best_modele_SVC.predict(X_test)
best_Y_test_pred_SVC
Out[ ]:
array([1, 0, 0, ..., 0, 0, 0])
In [ ]:
# Prédiction sur la base train
best_Y_train_pred_SVC = best_modele_SVC.predict(X_train)
best_Y_train_pred_SVC
Out[ ]:
array([1, 1, 0, ..., 1, 0, 1])
In [ ]:
# prediction en proba sur la base train
Y_pred_train_proba_SVC = best_modele_SVC.predict_proba(X_train)
Y_pred_train_proba_SVC
Out[ ]:
array([[8.67771555e-02, 9.13222845e-01],
       [1.76050770e-01, 8.23949230e-01],
       [8.96086596e-01, 1.03913404e-01],
       ...,
       [1.26854662e-01, 8.73145338e-01],
       [9.99648435e-01, 3.51564740e-04],
       [1.32489438e-01, 8.67510562e-01]])

Mesures de performance¶

Accuracy¶

In [ ]:
# Sur la base test
Acc_test_SVC = accuracy_score(Y_test, best_Y_test_pred_SVC) * 100
Acc_train_SVC = accuracy_score(Y_train, best_Y_train_pred_SVC) * 100
# Sur la base train
print("Accuracy sur train:", Acc_train_SVC, "%")
print("Accuracy sur test:", Acc_test_SVC, "%")

Accuracy sur train: 96.57604702424688 %
Accuracy sur test: 91.7723688721289 %

F1 score¶

In [ ]:
# Sur la base test et train
F1_score_test_SVC = f1_score(Y_test, best_Y_test_pred_SVC, average='weighted') * 100
F1_score_train_SVC = f1_score(Y_train, best_Y_train_pred_SVC, average='weighted') * 100
# Sur la base train
print("F1_score sur train:", F1_score_train_SVC, "%")
print("F1_score sur test:", F1_score_test_SVC, "%")

F1_score sur train: 96.57400009921112 %
F1_score sur test: 91.76016955331053 %

Précision¶

In [ ]:
# Sur la base test et train
precision_score_test_SVC = precision_score(Y_test, best_Y_test_pred_SVC, average='weighted') * 100
precision_score_train_SVC = precision_score(Y_train, best_Y_train_pred_SVC, average='weighted') * 100
# Sur la base train
print("Précision sur train:", precision_score_train_SVC, "%")
print("Précision sur test:", precision_score_test_SVC, "%")

Précision sur train: 96.67764691398 %
Précision sur test: 92.06033670088134 %

Recall¶

In [ ]:
# Sur la base test et train
recall_score_test_SVC = recall_score(Y_test, best_Y_test_pred_SVC, average='weighted') * 100
recall_score_train_SVC = recall_score(Y_train, best_Y_train_pred_SVC, average='weighted') * 100
# Sur la base train
print("Recall sur train:", recall_score_train_SVC, "%")
print("Recall sur test:", recall_score_test_SVC, "%")

Recall sur train: 96.57604702424688 %
Recall sur test: 91.7723688721289 %

Matrice de confusion¶

In [ ]:
matrice_SVM = confusion_matrix(Y_test, best_Y_test_pred_SVC)
# Affichage de la matrice de confusion avec titre
fig, ax = plot_confusion_matrix(conf_mat=matrice_SVM,
                                show_absolute=True,
                                show_normed=True,
                                colorbar=True)

plt.title("Confusion Matrix SVM")
plt.show()
print("Alors dans la classe 0,sur",Y_test.value_counts()[0],
      "individu,le modèle réussit à faire un bon classement sur", matrice_SVM[0,0],
      " individu et une erreur sur", matrice_SVM[0,1], "\n  Dans la classe 1,sur",Y_test.value_counts()[1],
      "individus le modèle fait un bon classement sur", matrice_SVM[1,1],
      " individu et une erreur sur", matrice_SVM[0,0] )
No description has been provided for this image 
Alors dans la classe 0,sur 1466 individu,le modèle réussit à faire un bon classement sur 1286  individu et une erreur sur 180 
  Dans la classe 1,sur 1451 individus le modèle fait un bon classement sur 1391  individu et une erreur sur 1286

*taux de bon et de mauvais classement*

In [ ]:
print(f"Taux de bon classement (Classe 0 - Sans AVC): {matrice_SVM[0, 0] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 0 - Sans AVC): {matrice_SVM[0, 1] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de bon classement (Classe 1 - Avec AVC): {matrice_SVM[1, 1] / Y_test.value_counts()[1] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 1 - Avec AVC): {matrice_SVM[1, 0] / Y_test.value_counts()[1] * 100:.2f} %")

Taux de bon classement (Classe 0 - Sans AVC): 87.72 %
Taux de mauvais classement (Classe 0 - Sans AVC): 12.28 %
Taux de bon classement (Classe 1 - Avec AVC): 95.86 %
Taux de mauvais classement (Classe 1 - Avec AVC): 4.14 %
In [ ]:
# Courbe ROC
fpr_SVC, tpr_SVC, thresholds_SVC = roc_curve(Y_test, best_modele_SVC.predict_proba(X_test)[:, 1])
roc_auc_SVC = auc(fpr_SVC, tpr_SVC)

plt.figure()
plt.plot(fpr_SVC, tpr_SVC, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc_SVC)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('Taux de faux positif')
plt.ylabel('Taux de vrai positif')
plt.title('Courbe de  ROC pour SVM')
plt.legend(loc="lower right")
plt.show()
No description has been provided for this image
In [ ]:
result = permutation_importance(best_modele_SVC, X, Y, n_repeats=10)
sorted_idx = result.importances_mean.argsort()

plt.barh(X.columns[sorted_idx], result.importances_mean[sorted_idx])
plt.xlabel("Importance des caractéristiques")
plt.show()
No description has been provided for this image

K-plusprochesvoisins(KNN)¶

optimisation des parametres¶

In [ ]:
# Définir le modèle et les paramètres
#modele_KNN=KNeighborsClassifier()
#param_grid = [{
 #   'n_neighbors': [5, 7, 9, 11],
  #  'weights': ['uniform', 'distance'],  # Poids uniformes ou pondérés
   #          'metric':['euclidean','manhattan']
    #         }
#]
# Créer l'objet GridSearchCV
#modele_opt_KNN = GridSearchCV(modele_KNN,# modèle initialisé
 #                        param_grid, # grilles de parametre du modèle
  #                       cv=10, # cross--validation
   #                      verbose=1 #longueur
    #                     )
# Entrainement du modèle
#modele_opt_KNN.fit(X_train,Y_train)
# parametre optimaux
#best_param = modele_opt_KNN.best_params_
#print("VOILA LES MEILLEURS PARAMÉTRES :",best_param)
# meilleur modèle
#best_modele_KNN = modele_opt_KNN.best_estimator_
#best_modele_KNN
In [ ]:
best_modele_KNN = KNeighborsClassifier(n_neighbors=7, weights='uniform', metric='euclidean')
best_modele_KNN.fit(X_train, Y_train)
Out[ ]:
KNeighborsClassifier(metric='euclidean', n_neighbors=7)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
KNeighborsClassifier(metric='euclidean', n_neighbors=7)
In [ ]:
#best_modele_KNN = KNeighborsClassifier(n_neighbors=7, weights='uniform', metric='manhattan')
#best_modele_KNN.fit(X_train, Y_train)

Prédiction¶

In [ ]:
# Prédiction sur la base test
best_Y_test_pred_KNN = best_modele_KNN.predict(X_test)
best_Y_test_pred_KNN
Out[ ]:
array([1, 0, 0, ..., 0, 0, 0])
In [ ]:
# Prédiction sur la base train
best_Y_train_pred_KNN = best_modele_KNN.predict(X_train)
best_Y_train_pred_KNN
Out[ ]:
array([1, 1, 0, ..., 1, 0, 1])
In [ ]:
# prediction en proba sur la base test
Y_pred_train_proba_KNN =best_modele_KNN.predict_proba(X_test)
Y_pred_train_proba_KNN
Out[ ]:
array([[0.14285714, 0.85714286],
       [1.        , 0.        ],
       [1.        , 0.        ],
       ...,
       [1.        , 0.        ],
       [1.        , 0.        ],
       [1.        , 0.        ]])

Mesure de performance¶

Accuracy¶

In [ ]:
# Sur la base test
Acc_test_KNN = accuracy_score(Y_test, best_Y_test_pred_KNN) * 100
Acc_train_KNN = accuracy_score(Y_train, best_Y_train_pred_KNN) * 100
# Sur la base train
print("Accuracy sur train:", Acc_train_KNN, "%")
print("Accuracy sur test:", Acc_test_KNN, "%")

Accuracy sur train: 89.33137398971344 %
Accuracy sur test: 87.35001714089819 %

F1 Score¶

In [ ]:
# Sur la base test et train
F1_score_test_KNN = f1_score(Y_test, best_Y_test_pred_KNN, average='weighted') * 100
F1_score_train_KNN = f1_score(Y_train, best_Y_train_pred_KNN, average='weighted') * 100
# Sur la base train
print("F1_score sur train:", F1_score_train_KNN, "%")
print("F1_score sur test:", F1_score_test_KNN, "%")

F1_score sur train: 89.24452031458395 %
F1_score sur test: 87.21720776325233 %

Précision¶

In [ ]:
# Sur la base test et train
precision_score_test_KNN = precision_score(Y_test, best_Y_test_pred_KNN, average='weighted') * 100
precision_score_train_KNN = precision_score(Y_train, best_Y_train_pred_KNN, average='weighted') * 100
# Sur la base train
print("Précision sur train:", precision_score_train_KNN, "%")
print("Précision sur test:", precision_score_test_KNN, "%")

Précision sur train: 90.60784379234124 %
Précision sur test: 89.06846256454828 %

Recall¶

In [ ]:
# Sur la base test et train
recall_score_test_KNN = recall_score(Y_test, best_Y_test_pred_KNN, average='weighted') * 100
recall_score_train_KNN = recall_score(Y_train, best_Y_train_pred_KNN, average='weighted') * 100
# Sur la base train
print("Recall sur train:", recall_score_train_KNN, "%")
print("Recall sur test:", recall_score_test_KNN, "%")

Recall sur train: 89.33137398971344 %
Recall sur test: 87.35001714089819 %

Matrice de confusion¶

In [ ]:
matrice_KNN = confusion_matrix(Y_test, best_Y_test_pred_SVC)
# Affichage de la matrice de confusion avec titre
fig, ax = plot_confusion_matrix(conf_mat=matrice_KNN,
                                show_absolute=True,
                                show_normed=True,
                                colorbar=True)

plt.title("Confusion Matrix KNN")
plt.show()
print("Alors dans la classe 0,sur",Y_test.value_counts()[0],
      "individu,le modèle réussit à faire un bon classement sur", matrice_KNN[0,0],
      " individu et une erreur sur", matrice_KNN[0,1], "\n  Dans la classe 1,sur",Y_test.value_counts()[1],
      "individus le modèle fait un bon classement sur", matrice_KNN[1,1],
      " individu et une erreur sur", matrice_KNN[0,0] )
No description has been provided for this image 
Alors dans la classe 0,sur 1466 individu,le modèle réussit à faire un bon classement sur 1286  individu et une erreur sur 180 
  Dans la classe 1,sur 1451 individus le modèle fait un bon classement sur 1391  individu et une erreur sur 1286

*taux de bon et de mauvais classement*

In [ ]:
print(f"Taux de bon classement (Classe 0 - Sans AVC): {matrice_KNN[0, 0] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 0 - Sans AVC): {matrice_KNN[0, 1] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de bon classement (Classe 1 - Avec AVC): {matrice_KNN[1, 1] / Y_test.value_counts()[1] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 1 - Avec AVC): {matrice_KNN[1, 0] / Y_test.value_counts()[1] * 100:.2f} %")

Taux de bon classement (Classe 0 - Sans AVC): 87.72 %
Taux de mauvais classement (Classe 0 - Sans AVC): 12.28 %
Taux de bon classement (Classe 1 - Avec AVC): 95.86 %
Taux de mauvais classement (Classe 1 - Avec AVC): 4.14 %
In [ ]:
# Courbe ROC
fpr_KNN, tpr_KNN, thresholds_SVC = roc_curve(Y_test, best_modele_KNN.predict_proba(X_test)[:, 1])
roc_auc_KNN = auc(fpr_KNN, tpr_KNN)

plt.figure()
plt.plot(fpr_KNN, tpr_KNN, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc_KNN)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('Taux de faux positif')
plt.ylabel('Taux de vrai positif')
plt.title('Courbe de  ROC pour KNN')
plt.legend(loc="lower right")
plt.show()
No description has been provided for this image
In [ ]:
result = permutation_importance(best_modele_KNN, X, Y, n_repeats=10)
sorted_idx = result.importances_mean.argsort()

plt.barh(X.columns[sorted_idx], result.importances_mean[sorted_idx])
plt.xlabel("Importance des caractéristiques")
plt.show()
No description has been provided for this image

Grandient Boosting¶

In [ ]:
# initialisation du modele
modele_GB = GradientBoostingClassifier()
param_grid_GB = ({
    'n_estimators': [100,200],
    'max_depth': [2,3,4,5],
    'learning_rate': [0.1,0.2],  # taux d'apprentissage
    'loss': ['log_loss'], # fonction d'erreur
    'subsample': [0.8, 1.0]        #Stochastic GB pour réduire la variance
     })
# modele optimal
modele_opt_GB = GridSearchCV(modele_GB,param_grid_GB,
                                      cv = 5,
                                      scoring='roc_auc')

Entrainement du modèle¶

In [ ]:
modele_opt_GB.fit(X_train,Y_train)
Out[ ]:
GridSearchCV(cv=5, estimator=GradientBoostingClassifier(),
             param_grid={'learning_rate': [0.1, 0.2], 'loss': ['log_loss'],
                         'max_depth': [2, 3, 4, 5], 'n_estimators': [100, 200],
                         'subsample': [0.8, 1.0]},
             scoring='roc_auc')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GridSearchCV(cv=5, estimator=GradientBoostingClassifier(),
             param_grid={'learning_rate': [0.1, 0.2], 'loss': ['log_loss'],
                         'max_depth': [2, 3, 4, 5], 'n_estimators': [100, 200],
                         'subsample': [0.8, 1.0]},
             scoring='roc_auc')
GradientBoostingClassifier(learning_rate=0.2, max_depth=5, n_estimators=200,
                           subsample=0.8)
GradientBoostingClassifier(learning_rate=0.2, max_depth=5, n_estimators=200,
                           subsample=0.8)
In [ ]:
# parametres optimaux
best_param_GB = modele_opt_GB.best_params_
print("VOILA LES MEILLEURS PARAMÉTRES :",best_param_GB)
# best modele
best_modele_GB = modele_opt_GB.best_estimator_
best_modele_GB

VOILA LES MEILLEURS PARAMÉTRES : {'learning_rate': 0.2, 'loss': 'log_loss', 'max_depth': 5, 'n_estimators': 200, 'subsample': 0.8}
Out[ ]:
GradientBoostingClassifier(learning_rate=0.2, max_depth=5, n_estimators=200,
                           subsample=0.8)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GradientBoostingClassifier(learning_rate=0.2, max_depth=5, n_estimators=200,
                           subsample=0.8)

Prédiction¶

In [ ]:
 best_Y_test_pred_GB = best_modele_GB.predict(X_test)
 best_Y_test_pred_GB
Out[ ]:
array([1, 0, 0, ..., 0, 0, 0])
In [ ]:
 best_Y_train_pred_GB = best_modele_GB.predict(X_train)
 best_Y_train_pred_GB
Out[ ]:
array([1, 1, 0, ..., 1, 0, 1])

Mesure de performance¶

Accuracy¶

In [ ]:
# Sur la base test
Acc_test_GB = accuracy_score(Y_test, best_Y_test_pred_GB) * 100
Acc_train_GB = accuracy_score(Y_train, best_Y_train_pred_GB) * 100
# Sur la base train
print("Accuracy sur train:", Acc_train_GB, "%")
print("Accuracy sur test:", Acc_test_GB, "%")

Accuracy sur train: 99.85304922850845 %
Accuracy sur test: 95.33767569420638 %

F1 score¶

In [ ]:
# Sur la base test et train
F1_score_test_GB = f1_score(Y_test, best_Y_test_pred_GB, average='weighted') * 100
F1_score_train_GB = f1_score(Y_train, best_Y_train_pred_GB, average='weighted') * 100
# Sur la base train
print("F1_score sur train:", F1_score_train_GB, "%")
print("F1_score sur test:", F1_score_test_GB, "%")

F1_score sur train: 99.85304936813544 %
F1_score sur test: 95.33589401841132 %

Précision¶

In [ ]:
# Sur la base test et train
precision_score_test_GB = precision_score(Y_test, best_Y_test_pred_GB, average='weighted') * 100
precision_score_train_GB = precision_score(Y_train, best_Y_train_pred_GB, average='weighted') * 100
# Sur la base train
print("Précision sur train:", precision_score_train_GB, "%")
print("Précision sur test:", precision_score_test_GB, "%")

Précision sur train: 99.85311850878198 %
Précision sur test: 95.39021817593391 %

Recall¶

In [ ]:
# Sur la base test et train
recall_score_test_GB = recall_score(Y_test, best_Y_test_pred_GB, average='weighted') * 100
recall_score_train_GB = recall_score(Y_train, best_Y_train_pred_GB, average='weighted') * 100
# Sur la base train
print("Recall sur train:", recall_score_train_GB, "%")
print("Recall sur test:", recall_score_test_GB, "%")

Recall sur train: 99.85304922850845 %
Recall sur test: 95.33767569420638 %

Matrice de confusion¶

In [ ]:
matrice_GB = confusion_matrix(Y_test, best_Y_test_pred_SVC)
# Affichage de la matrice de confusion avec titre
fig, ax = plot_confusion_matrix(conf_mat=matrice_GB,
                                show_absolute=True,
                                show_normed=True,
                                colorbar=True)

plt.title("Confusion Matrix GB")
plt.show()
print("Alors dans la classe 0,sur",Y_test.value_counts()[0],
      "individu,le modèle réussit à faire un bon classement sur", matrice_GB[0,0],
      " individu et une erreur sur", matrice_GB[0,1], "\n  Dans la classe 1,sur",Y_test.value_counts()[1],
      "individus le modèle fait un bon classement sur", matrice_GB[1,1],
      " individu et une erreur sur", matrice_GB[0,0] )
No description has been provided for this image 
Alors dans la classe 0,sur 1466 individu,le modèle réussit à faire un bon classement sur 1286  individu et une erreur sur 180 
  Dans la classe 1,sur 1451 individus le modèle fait un bon classement sur 1391  individu et une erreur sur 1286

*taux de bon et de mauvais classement*

In [ ]:
print(f"Taux de bon classement (Classe 0 - Sans AVC): {matrice_GB[0, 0] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 0 - Sans AVC): {matrice_GB[0, 1] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de bon classement (Classe 1 - Avec AVC): {matrice_GB[1, 1] / Y_test.value_counts()[1] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 1 - Avec AVC): {matrice_GB[1, 0] / Y_test.value_counts()[1] * 100:.2f} %")

Taux de bon classement (Classe 0 - Sans AVC): 87.72 %
Taux de mauvais classement (Classe 0 - Sans AVC): 12.28 %
Taux de bon classement (Classe 1 - Avec AVC): 95.86 %
Taux de mauvais classement (Classe 1 - Avec AVC): 4.14 %
In [ ]:
# Courbe ROC
fpr_GB, tpr_GB, thresholds_SVC = roc_curve(Y_test, best_modele_GB.predict_proba(X_test)[:, 1])
roc_auc_GB = auc(fpr_GB, tpr_GB)

plt.figure()
plt.plot(fpr_GB, tpr_GB, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc_GB)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('Taux de faux positif')
plt.ylabel('Taux de vrai positif')
plt.title('Courbe de  ROC pour Grandient Boosting')
plt.legend(loc="lower right")
plt.show()
No description has been provided for this image
In [ ]:
from sklearn.model_selection import learning_curve
import matplotlib.pyplot as plt
import numpy as np

train_sizes, train_scores, val_scores = learning_curve(
    estimator=best_modele_GB, X=X_train, y=Y_train, cv=5,
    scoring="roc_auc", train_sizes=np.linspace(0.1, 1.0, 10)
)

plt.plot(train_sizes, np.mean(train_scores, axis=1), label="Train")
plt.plot(train_sizes, np.mean(val_scores, axis=1), label="Validation")
plt.xlabel("Training Set Size")
plt.ylabel("AUC Score")
plt.legend()
plt.show()
No description has been provided for this image
In [ ]:
result = permutation_importance(best_modele_GB, X, Y, n_repeats=10)
sorted_idx = result.importances_mean.argsort()

plt.barh(X.columns[sorted_idx], result.importances_mean[sorted_idx])
plt.xlabel("Importance des caractéristiques")
plt.show()
No description has been provided for this image

Réseau de Neurone¶

In [ ]:
#from tensorflow.keras.models import Sequential
#from tensorflow.keras.layers import Dense, Dropout, BatchNormalization
#from tensorflow.keras.optimizers import Adam
#from tensorflow.keras.callbacks import EarlyStopping

#model_base = Sequential([
 #   Dense(128, activation='relu', input_shape=(X_train.shape[1],)),
  #  BatchNormalization(),
   # Dropout(0.3),  # Réduit le surajustement
   # Dense(64, activation='relu'),
   # BatchNormalization(),
   # Dropout(0.2),
   # Dense(32, activation='relu'),
   # BatchNormalization(),
   # Dense(1, activation='sigmoid')
#])

*Création d'un modèle de base avec 4 couches cachées*

In [ ]:
# Conversion en float32
X_train = X_train.astype(np.float32)
Y_train = Y_train.astype(np.float32)
X_test = X_test.astype(np.float32)
Y_test = Y_test.astype(np.float32)

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, BatchNormalization
from tensorflow.keras.optimizers import Adam

model_base = Sequential([
    Dense(128, input_shape=(X_train.shape[1],), activation='relu'),
    BatchNormalization(),
    Dropout(0.2),
    Dense(64, activation='relu'),
    BatchNormalization(),
    Dropout(0.2),
    Dense(64, activation='relu'),
    BatchNormalization(),
    Dropout(0.2),
    Dense(32, activation='relu'),
    BatchNormalization(),
    Dropout(0.2),
    Dense(32, activation='relu'),
    BatchNormalization(),
    Dropout(0.2),
    Dense(1, activation='sigmoid')
])
# Construction et entraînement du modèle
model_base.compile(
    optimizer=Adam(learning_rate=0.001),
    loss='binary_crossentropy',
    metrics=['accuracy'])


history_base = model_base.fit(
    X_train, Y_train,
    validation_data=(X_test, Y_test),
    epochs=100,
    batch_size=32,
    verbose=1
)

/usr/local/lib/python3.11/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)

Epoch 1/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 9s 7ms/step - accuracy: 0.6901 - loss: 0.6063 - val_accuracy: 0.7875 - val_loss: 0.4693
Epoch 2/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.7593 - loss: 0.5027 - val_accuracy: 0.8180 - val_loss: 0.4128
Epoch 3/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.7722 - loss: 0.4708 - val_accuracy: 0.8204 - val_loss: 0.3989
Epoch 4/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.7869 - loss: 0.4574 - val_accuracy: 0.8228 - val_loss: 0.3887
Epoch 5/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.7852 - loss: 0.4549 - val_accuracy: 0.8348 - val_loss: 0.3730
Epoch 6/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.8071 - loss: 0.4179 - val_accuracy: 0.8433 - val_loss: 0.3693
Epoch 7/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.8005 - loss: 0.4265 - val_accuracy: 0.8430 - val_loss: 0.3644
Epoch 8/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8187 - loss: 0.4173 - val_accuracy: 0.8375 - val_loss: 0.3623
Epoch 9/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.8068 - loss: 0.4169 - val_accuracy: 0.8450 - val_loss: 0.3541
Epoch 10/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8130 - loss: 0.4147 - val_accuracy: 0.8533 - val_loss: 0.3530
Epoch 11/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.8273 - loss: 0.3957 - val_accuracy: 0.8570 - val_loss: 0.3439
Epoch 12/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8294 - loss: 0.3917 - val_accuracy: 0.8557 - val_loss: 0.3402
Epoch 13/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.8300 - loss: 0.3867 - val_accuracy: 0.8481 - val_loss: 0.3467
Epoch 14/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8225 - loss: 0.3950 - val_accuracy: 0.8519 - val_loss: 0.3451
Epoch 15/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.8250 - loss: 0.3866 - val_accuracy: 0.8468 - val_loss: 0.3468
Epoch 16/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.8205 - loss: 0.3985 - val_accuracy: 0.8557 - val_loss: 0.3343
Epoch 17/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8281 - loss: 0.3837 - val_accuracy: 0.8560 - val_loss: 0.3348
Epoch 18/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8348 - loss: 0.3784 - val_accuracy: 0.8598 - val_loss: 0.3309
Epoch 19/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - accuracy: 0.8324 - loss: 0.3787 - val_accuracy: 0.8560 - val_loss: 0.3339
Epoch 20/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.8288 - loss: 0.3800 - val_accuracy: 0.8632 - val_loss: 0.3265
Epoch 21/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.8355 - loss: 0.3715 - val_accuracy: 0.8718 - val_loss: 0.3221
Epoch 22/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.8397 - loss: 0.3690 - val_accuracy: 0.8646 - val_loss: 0.3303
Epoch 23/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.8404 - loss: 0.3667 - val_accuracy: 0.8570 - val_loss: 0.3305
Epoch 24/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.8375 - loss: 0.3792 - val_accuracy: 0.8697 - val_loss: 0.3165
Epoch 25/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8537 - loss: 0.3552 - val_accuracy: 0.8673 - val_loss: 0.3181
Epoch 26/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.8407 - loss: 0.3657 - val_accuracy: 0.8684 - val_loss: 0.3209
Epoch 27/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.8475 - loss: 0.3569 - val_accuracy: 0.8684 - val_loss: 0.3205
Epoch 28/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.8519 - loss: 0.3474 - val_accuracy: 0.8680 - val_loss: 0.3218
Epoch 29/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8405 - loss: 0.3628 - val_accuracy: 0.8684 - val_loss: 0.3256
Epoch 30/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8457 - loss: 0.3596 - val_accuracy: 0.8636 - val_loss: 0.3167
Epoch 31/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step - accuracy: 0.8523 - loss: 0.3501 - val_accuracy: 0.8752 - val_loss: 0.3130
Epoch 32/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8567 - loss: 0.3403 - val_accuracy: 0.8745 - val_loss: 0.3155
Epoch 33/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8557 - loss: 0.3401 - val_accuracy: 0.8749 - val_loss: 0.3105
Epoch 34/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8512 - loss: 0.3563 - val_accuracy: 0.8697 - val_loss: 0.3142
Epoch 35/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 8ms/step - accuracy: 0.8470 - loss: 0.3537 - val_accuracy: 0.8636 - val_loss: 0.3243
Epoch 36/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.8481 - loss: 0.3528 - val_accuracy: 0.8749 - val_loss: 0.3086
Epoch 37/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.8583 - loss: 0.3386 - val_accuracy: 0.8745 - val_loss: 0.3071
Epoch 38/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8542 - loss: 0.3459 - val_accuracy: 0.8821 - val_loss: 0.3042
Epoch 39/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8451 - loss: 0.3491 - val_accuracy: 0.8807 - val_loss: 0.3022
Epoch 40/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8487 - loss: 0.3480 - val_accuracy: 0.8711 - val_loss: 0.3073
Epoch 41/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8561 - loss: 0.3377 - val_accuracy: 0.8790 - val_loss: 0.3053
Epoch 42/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.8585 - loss: 0.3366 - val_accuracy: 0.8735 - val_loss: 0.3101
Epoch 43/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - accuracy: 0.8530 - loss: 0.3350 - val_accuracy: 0.8797 - val_loss: 0.3009
Epoch 44/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 6ms/step - accuracy: 0.8626 - loss: 0.3360 - val_accuracy: 0.8903 - val_loss: 0.2932
Epoch 45/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8617 - loss: 0.3225 - val_accuracy: 0.8855 - val_loss: 0.2974
Epoch 46/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8551 - loss: 0.3407 - val_accuracy: 0.8786 - val_loss: 0.3010
Epoch 47/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8543 - loss: 0.3421 - val_accuracy: 0.8852 - val_loss: 0.2987
Epoch 48/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8585 - loss: 0.3354 - val_accuracy: 0.8855 - val_loss: 0.2978
Epoch 49/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8701 - loss: 0.3177 - val_accuracy: 0.8738 - val_loss: 0.3036
Epoch 50/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 9ms/step - accuracy: 0.8580 - loss: 0.3326 - val_accuracy: 0.8780 - val_loss: 0.2982
Epoch 51/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8697 - loss: 0.3176 - val_accuracy: 0.8828 - val_loss: 0.2976
Epoch 52/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8676 - loss: 0.3198 - val_accuracy: 0.8848 - val_loss: 0.2955
Epoch 53/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step - accuracy: 0.8609 - loss: 0.3321 - val_accuracy: 0.8845 - val_loss: 0.2940
Epoch 54/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8568 - loss: 0.3351 - val_accuracy: 0.8738 - val_loss: 0.3049
Epoch 55/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 6ms/step - accuracy: 0.8680 - loss: 0.3098 - val_accuracy: 0.8862 - val_loss: 0.2908
Epoch 56/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8613 - loss: 0.3348 - val_accuracy: 0.8896 - val_loss: 0.2923
Epoch 57/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.8685 - loss: 0.3119 - val_accuracy: 0.8769 - val_loss: 0.3006
Epoch 58/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.8663 - loss: 0.3155 - val_accuracy: 0.8845 - val_loss: 0.2921
Epoch 59/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.8632 - loss: 0.3367 - val_accuracy: 0.8869 - val_loss: 0.2957
Epoch 60/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.8673 - loss: 0.3137 - val_accuracy: 0.8855 - val_loss: 0.2898
Epoch 61/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8695 - loss: 0.3160 - val_accuracy: 0.8821 - val_loss: 0.2957
Epoch 62/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8653 - loss: 0.3215 - val_accuracy: 0.8882 - val_loss: 0.2810
Epoch 63/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - accuracy: 0.8615 - loss: 0.3269 - val_accuracy: 0.8831 - val_loss: 0.2941
Epoch 64/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 8ms/step - accuracy: 0.8636 - loss: 0.3249 - val_accuracy: 0.8756 - val_loss: 0.3012
Epoch 65/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.8657 - loss: 0.3257 - val_accuracy: 0.8910 - val_loss: 0.2890
Epoch 66/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.8560 - loss: 0.3248 - val_accuracy: 0.8858 - val_loss: 0.2915
Epoch 67/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step - accuracy: 0.8645 - loss: 0.3202 - val_accuracy: 0.8869 - val_loss: 0.2859
Epoch 68/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8766 - loss: 0.3059 - val_accuracy: 0.8862 - val_loss: 0.2871
Epoch 69/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - accuracy: 0.8687 - loss: 0.3117 - val_accuracy: 0.8797 - val_loss: 0.2998
Epoch 70/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 8ms/step - accuracy: 0.8711 - loss: 0.3185 - val_accuracy: 0.8900 - val_loss: 0.2868
Epoch 71/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 9ms/step - accuracy: 0.8751 - loss: 0.3114 - val_accuracy: 0.8848 - val_loss: 0.2913
Epoch 72/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.8728 - loss: 0.3130 - val_accuracy: 0.8800 - val_loss: 0.2977
Epoch 73/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step - accuracy: 0.8707 - loss: 0.3094 - val_accuracy: 0.8759 - val_loss: 0.2985
Epoch 74/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8644 - loss: 0.3161 - val_accuracy: 0.8917 - val_loss: 0.2804
Epoch 75/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - accuracy: 0.8786 - loss: 0.2985 - val_accuracy: 0.8920 - val_loss: 0.2827
Epoch 76/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.8762 - loss: 0.3097 - val_accuracy: 0.8804 - val_loss: 0.2896
Epoch 77/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.8677 - loss: 0.3183 - val_accuracy: 0.8920 - val_loss: 0.2869
Epoch 78/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.8714 - loss: 0.3070 - val_accuracy: 0.8845 - val_loss: 0.2865
Epoch 79/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8678 - loss: 0.3172 - val_accuracy: 0.8951 - val_loss: 0.2744
Epoch 80/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8588 - loss: 0.3322 - val_accuracy: 0.8906 - val_loss: 0.2826
Epoch 81/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8785 - loss: 0.3101 - val_accuracy: 0.8848 - val_loss: 0.2887
Epoch 82/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.8774 - loss: 0.3000 - val_accuracy: 0.8903 - val_loss: 0.2885
Epoch 83/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.8656 - loss: 0.3235 - val_accuracy: 0.8903 - val_loss: 0.2847
Epoch 84/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8749 - loss: 0.3060 - val_accuracy: 0.8882 - val_loss: 0.2830
Epoch 85/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 9ms/step - accuracy: 0.8776 - loss: 0.3031 - val_accuracy: 0.8780 - val_loss: 0.2921
Epoch 86/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.8695 - loss: 0.3099 - val_accuracy: 0.8828 - val_loss: 0.2870
Epoch 87/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.8709 - loss: 0.3142 - val_accuracy: 0.8872 - val_loss: 0.2790
Epoch 88/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step - accuracy: 0.8771 - loss: 0.2898 - val_accuracy: 0.8838 - val_loss: 0.2832
Epoch 89/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step - accuracy: 0.8759 - loss: 0.3009 - val_accuracy: 0.8937 - val_loss: 0.2798
Epoch 90/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8733 - loss: 0.3139 - val_accuracy: 0.8900 - val_loss: 0.2801
Epoch 91/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 6ms/step - accuracy: 0.8879 - loss: 0.2818 - val_accuracy: 0.8886 - val_loss: 0.2845
Epoch 92/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.8688 - loss: 0.3092 - val_accuracy: 0.8786 - val_loss: 0.2915
Epoch 93/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8737 - loss: 0.3156 - val_accuracy: 0.8903 - val_loss: 0.2825
Epoch 94/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8789 - loss: 0.2964 - val_accuracy: 0.8831 - val_loss: 0.2883
Epoch 95/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8764 - loss: 0.2973 - val_accuracy: 0.8797 - val_loss: 0.2889
Epoch 96/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8774 - loss: 0.3014 - val_accuracy: 0.8879 - val_loss: 0.2801
Epoch 97/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8668 - loss: 0.3228 - val_accuracy: 0.8924 - val_loss: 0.2753
Epoch 98/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8889 - loss: 0.2796 - val_accuracy: 0.8893 - val_loss: 0.2820
Epoch 99/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 8ms/step - accuracy: 0.8682 - loss: 0.3181 - val_accuracy: 0.8865 - val_loss: 0.2845
Epoch 100/100
213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.8852 - loss: 0.2909 - val_accuracy: 0.8903 - val_loss: 0.2777

Prédiction

In [ ]:
# Obtenir les prédictions
y_train_pred_prob = model_base.predict(X_train)
y_test_pred_prob = model_base.predict(X_test)

# Convertir les probabilités en classes (0 ou 1)
y_train_pred = (y_train_pred_prob > 0.5).astype(int)
y_test_pred = (y_test_pred_prob > 0.5).astype(int)

213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step
92/92 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step

Mesure de performance

Accuracy

In [ ]:
train_accuracy = accuracy_score(Y_train, y_train_pred)* 100
test_accuracy = accuracy_score(Y_test, y_test_pred)* 100
print(f"Accuracy sur la base train: {train_accuracy:.4f}")
print(f"Accuracy sur la base test: {test_accuracy:.4f}")

Accuracy sur la base train: 90.9037
Accuracy sur la base test: 89.0298

Précision

In [ ]:
train_precision = precision_score(Y_train, y_train_pred)* 100
test_precision = precision_score(Y_test, y_test_pred)* 100
print(f"Precision sur la base train: {train_precision:.4f}")
print(f"Precision sur la base test: {test_precision:.4f}")

Precision sur la base train: 85.2845
Precision sur la base test: 83.5608

F1-score

In [ ]:
train_f1 = f1_score(Y_train, y_train_pred)* 100
test_f1 = f1_score(Y_test, y_test_pred)* 100
print(f"F1-score sur la base train: {train_f1:.4f}")
print(f"F1-score sur la base test: {test_f1:.4f}")

F1-score sur la base train: 91.5954
F1-score sur la base test: 89.7959

Recall

In [ ]:
train_recall = recall_score(Y_train, y_train_pred)* 100
test_recall = recall_score(Y_test, y_test_pred)* 100
print(f"Recall sur la base train: {train_recall:.4f}")
print(f"Recall sur la base test: {test_recall:.4f}")

Recall sur la base train: 98.9150
Recall sur la base test: 97.0365

Matrice de confusion

In [ ]:
# Matrice de confusion pour le test
matrice_RN = confusion_matrix(Y_test, y_test_pred)

plt.figure(figsize=(8, 6))
sns.heatmap(matrice_RN, annot=True, fmt='d', cmap='Blues',
            xticklabels=['Prédit 0', 'Prédit 1'],
            yticklabels=['Vrai 0', 'Vrai 1'])
plt.title('Matrice de confusion - Réseau de neurone')
plt.ylabel('Vraie classe')
plt.xlabel('Classe prédite')
plt.show()
No description has been provided for this image

Taux de bon classement

In [ ]:
print(f"Taux de bon classement (Classe 0 - Sans AVC): {matrice_RN[0, 0] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 0 - Sans AVC): {matrice_RN[0, 1] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de bon classement (Classe 1 - Avec AVC): {matrice_RN[1, 1] / Y_test.value_counts()[1] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 1 - Avec AVC): {matrice_RN[1, 0] / Y_test.value_counts()[1] * 100:.2f} %")

Taux de bon classement (Classe 0 - Sans AVC): 81.11 %
Taux de mauvais classement (Classe 0 - Sans AVC): 18.89 %
Taux de bon classement (Classe 1 - Avec AVC): 97.04 %
Taux de mauvais classement (Classe 1 - Avec AVC): 2.96 %

Courbe de ROC

In [ ]:
# Calcul des métriques ROC
from sklearn.metrics import roc_curve, roc_auc_score
fpr, tpr, thresholds = roc_curve(Y_test, y_test_pred_prob)
auc_score = roc_auc_score(Y_test, y_test_pred_prob)

# Visualisation comparative des courbes ROC
plt.figure(figsize=(8, 6))
plt.plot(fpr, tpr, color='green', lw=2, label=f'ROC curve (AUC = {auc_score:.2f})')
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('Taux de faux positifs')
plt.ylabel('Taux de vrais positifs')
plt.title('Courbes ROC')
plt.legend(loc="lower right")
plt.show()
No description has been provided for this image
In [ ]:
# Courbes d'apprentissage
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(history_base.history['accuracy'], label='Train Accuracy')
plt.plot(history_base.history['val_accuracy'], label='Validation Accuracy')
plt.title('Évolution de l\'Accuracy')
plt.xlabel('Époque')
plt.ylabel('Précision')
plt.legend()

plt.subplot(1, 2, 2)
plt.plot(history_base.history['loss'], label='Train Loss')
plt.plot(history_base.history['val_loss'], label='Validation Loss')
plt.title("Courbe de Loss au cours de l'entraînement")
plt.xlabel('Époque')
plt.ylabel('Perte')
plt.legend()
plt.show()
No description has been provided for this image
In [ ]:
modele = ["RL","AD","RF","SVM","KNN","GB","RN"]

accuracy_scores=[Acc_test_RL,Acc_test_AD, Acc_test_RF, Acc_test_SVC,Acc_test_KNN,Acc_test_GB,test_accuracy]

matrice_test=[matrice_RL,matrice_AD,matrice_RF,matrice_SVM,matrice_KNN,matrice_GB,matrice_RN]

precision_scores=[precision_score_test_RL,precision_score_test_AD,precision_score_test_RF,
                                        precision_score_test_SVC,precision_score_test_KNN,precision_score_test_GB, test_precision]

recall_scores=[recall_score_test_RL,recall_score_test_AD,recall_score_test_RF,recall_score_test_SVC,recall_score_test_KNN,recall_score_test_GB,test_recall]

F1_scores=[F1_score_test_RL,F1_score_test_AD,F1_score_test_RF,F1_score_test_SVC,F1_score_test_KNN,F1_score_test_GB,test_f1]

metrics_df = pd.DataFrame({
    "Modèle": modele,
    "Accuracy": accuracy_scores,
    "Precision": precision_scores,
    "Recall": recall_scores,
    "F1 Score": F1_scores
})

# Arrondir pour lisibilité
metrics_df[["Accuracy", "Precision", "Recall", "F1 Score"]] = metrics_df[["Accuracy", "Precision", "Recall", "F1 Score"]].round(2)
# Triez les résultats en ordre décroissant
metrics_df = metrics_df.sort_values(by="Recall", ascending=False)
# Affichez les résultats
print(metrics_df)

  Modèle  Accuracy  Precision  Recall  F1 Score
6     RN     89.03      83.56   97.04     89.80
5     GB     95.34      95.39   95.34     95.34
3    SVM     91.77      92.06   91.77     91.76
4    KNN     87.35      89.07   87.35     87.22
2     RF     87.01      87.66   87.01     86.96
1     AD     84.64      85.58   84.64     84.55
0     RL     78.03      78.19   78.03     78.00
In [ ]:
taux_bon_classement_0 = {
    "Regression Logistique": matrice_RL[0, 0] / Y_test.value_counts()[0] * 100,
    "Arbre de Décision": matrice_AD[0, 0] / Y_test.value_counts()[0] * 100,
    "Random Forest": matrice_RF[0, 0] / Y_test.value_counts()[0] * 100,
    "SVM": matrice_SVM[0, 0] / Y_test.value_counts()[0] * 100,
    "KNN": matrice_KNN[0, 0] / Y_test.value_counts()[0] * 100,
    "Gradient Boosting": matrice_GB[0, 0] / Y_test.value_counts()[0] * 100,
    "Réseau de neurone": matrice_RN[0, 0] / Y_test.value_counts()[0] * 100
}

taux_bon_classement_1 = {
    "Regression Logistique": matrice_RL[1, 1] / Y_test.value_counts()[1] * 100,
    "Arbre de Décision": matrice_AD[1, 1] / Y_test.value_counts()[1] * 100,
    "Random Forest": matrice_RF[1, 1] / Y_test.value_counts()[1] * 100,
    "SVM": matrice_SVM[1, 1] / Y_test.value_counts()[1] * 100,
    "KNN": matrice_KNN[1, 1] / Y_test.value_counts()[1] * 100,
    "Gradient Boosting": matrice_GB[1, 1] / Y_test.value_counts()[1] * 100,
    "Réseau de neurone": matrice_RN[1, 1] / Y_test.value_counts()[1] * 100

}

taux_mal_classement_0 = {
    "Regression Logistique": matrice_RL[0, 1] / Y_test.value_counts()[0] * 100,
    "Arbre de Décision": matrice_AD[0, 1] / Y_test.value_counts()[0] * 100,
    "Random Forest": matrice_RF[0, 1] / Y_test.value_counts()[0] * 100,
    "SVM": matrice_SVM[0, 1] / Y_test.value_counts()[0] * 100,
    "KNN": matrice_KNN[0, 1] / Y_test.value_counts()[0] * 100,
    "Gradient Boosting": matrice_GB[0, 1] / Y_test.value_counts()[0] * 100,
    "Réseau de neurone": matrice_RN[0, 1] / Y_test.value_counts()[0] * 100

}

taux_mal_classement_1 = {
    "Regression Logistique": matrice_RL[1, 0] / Y_test.value_counts()[1] * 100,
    "Arbre de Décision": matrice_AD[1, 0] / Y_test.value_counts()[1] * 100,
    "Random Forest": matrice_RF[1, 0] / Y_test.value_counts()[1] * 100,
    "SVM": matrice_SVM[1, 0] / Y_test.value_counts()[1] * 100,
    "KNN": matrice_KNN[1, 0] / Y_test.value_counts()[1] * 100,
    "Gradient Boosting": matrice_GB[1, 0] / Y_test.value_counts()[1] * 100,
    "Réseau de neurone": matrice_RN[1, 0] / Y_test.value_counts()[1] * 100

}


summary_classification_rates = pd.DataFrame({
    "Modèle": list(taux_bon_classement_0.keys()),
    "Taux Bon Classement (Classe 0)": list(taux_bon_classement_0.values()),
    "Taux Mal Classement (Classe 0)": list(taux_mal_classement_0.values()),
    "Taux Bon Classement (Classe 1)": list(taux_bon_classement_1.values()),
    "Taux Mal Classement (Classe 1)": list(taux_mal_classement_1.values()),

})

# Arrondir pour lisibilité
summary_classification_rates[['Taux Bon Classement (Classe 0)', 'Taux Mal Classement (Classe 0)', 'Taux Bon Classement (Classe 1)', 'Taux Mal Classement (Classe 1)']] = summary_classification_rates[['Taux Bon Classement (Classe 0)', 'Taux Mal Classement (Classe 0)', 'Taux Bon Classement (Classe 1)', 'Taux Mal Classement (Classe 1)']].round(2)


print("\n--- Résumé des Taux de Classement par Modèle ---")
display(summary_classification_rates)

--- Résumé des Taux de Classement par Modèle ---
Modèle Taux Bon Classement (Classe 0) Taux Mal Classement (Classe 0) Taux Bon Classement (Classe 1) Taux Mal Classement (Classe 1)
0 Regression Logistique 74.49 25.51 81.60 18.40
1 Arbre de Décision 76.67 23.33 92.69 7.31
2 Random Forest 80.56 19.44 93.52 6.48
3 SVM 87.72 12.28 95.86 4.14
4 KNN 87.72 12.28 95.86 4.14
5 Gradient Boosting 87.72 12.28 95.86 4.14
6 Réseau de neurone 81.11 18.89 97.04 2.96